Dockerized build

master
J. Fernando Sánchez 6 years ago
parent 19ccc8b9f1
commit 5983fe23f6

@ -1,4 +1,29 @@
from python:3.5
WORKDIR /usr/src/app
ADD requirements.txt .
RUN pip install -r requirements.txt
ADD create_bibliography.py ref.bib resume-nobib.json /usr/src/app/
RUN python create_bibliography.py
from node
RUN npm install -g resume-cli jsonresume-theme-elegant
WORKDIR /usr/src/app
COPY --from=0 /usr/src/app/resume.json /usr/src/app/
RUN resume export --theme elegant --format html index.html
from nginx
COPY --from=1 /usr/src/app/index.html /usr/src/app/resume.json /usr/share/nginx/html/
EXPOSE 80
ADD . /usr/share/nginx/html/

@ -1,22 +1,31 @@
import bibtexparser
from bibtexparser.customization import *
from datetime import datetime
import json
with open("resume-nobib.json") as r:
resume = json.load(r)
parser = bibtexparser.bparser.BibTexParser(common_strings=True,
customization=convert_to_unicode)
with open('ref.bib') as bibtex_file:
parser = bibtexparser.bparser.BibTexParser()
parser.customization = convert_to_unicode
bibtex_str = bibtex_file.read()
bib_database = bibtexparser.loads(bibtex_str)
bib_database = bibtexparser.loads(bibtex_str, parser=parser)
pubs = []
for entry in bib_database.entries:
pub = {}
if "booktitle" in entry:
pub["publisher"] = journal(entry["booktitle"])
if "year" in entry:
pub["releaseDate"] = entry["year"]
reldate = datetime.strptime('{}-{}-{}'.format(entry.get('year', 2010),
entry.get('month', 'January'),
entry.get('day', 1)),
'%Y-%B-%d')
pub["releaseDate"] = reldate.strftime('%Y-%m-%d')
print(pub['releaseDate'])
if "author" in entry:
pub["authors"] = author(entry["author"])
if "abstract" in entry:

File diff suppressed because one or more lines are too long

@ -1,223 +1,288 @@
@conference{araqueTASS2015,
author = "Oscar Araque and Ignacio Corcuera-Platas and Constantino Rom{\'a}n-G{\'o}mez and Carlos A. Iglesias and J. Fernando S{\'a}nchez-Rada",
abstract = "This article presents the participation of the Intelligent Systems Group
(GSI) at Universidad Polit´ecnica de Madrid (UPM) in the Sentiment Analysis workshop
focused in Spanish tweets, TASS2015. This year two challenges have been
proposed, which we have addressed with the design and development of a modular
system that is adaptable to different contexts. This system employs Natural
Language Processing (NLP) and machine-learning technologies, relying also in previously
developed technologies in our research group. In particular, we have used a
wide number of features and polarity lexicons for sentiment detection. With regards
to aspect detection, we have relied on a graph-based algorithm. Once the challenge
has come to an end, the experimental results are promising.",
booktitle = "Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN co-located with 31st SEPLN Conference",
keywords = "Machine Learning, Natural Language Processing, Sentiment analysis, Aspect detection",
month = "September",
pages = "29-34",
title = "{A}spect based {S}entiment {A}nalysis of {S}panish {T}weets",
url = "http://ceur-ws.org/Vol-1397/gsi.pdf",
volume = "1397",
year = "2015",
}
@conference{220151,
author = "J. Fernando S{\'a}nchez-Rada and Carlos A. Iglesias and Ronald Gil",
address = "Beijing, China",
keywords = "sanchezrada;iglesias",
month = "July",
pages = "11--19",
title = "{A} {L}inked {D}ata {M}odel for {M}ultimodal {S}entiment and {E}motion {A}nalysis",
url = "http://aclweb.org/anthology/W/W15/W15-4202.pdf",
year = "2015",
}
@conference{220151,
author = "J. Fernando S{\'a}nchez-Rada and Carlos A. Iglesias and Ronald Gil",
address = "Beijing, China",
keywords = "sanchezrada;iglesias",
month = "July",
pages = "11--19",
title = "{A} {L}inked {D}ata {M}odel for {M}ultimodal {S}entiment and {E}motion {A}nalysis",
url = "http://aclweb.org/anthology/W/W15/W15-4202.pdf",
year = "2015",
}
@conference{araqueTASS2015,
author = "Oscar Araque and Ignacio Corcuera-Platas and Constantino Rom{\'a}n-G{\'o}mez and Carlos A. Iglesias and J. Fernando S{\'a}nchez-Rada",
abstract = "This article presents the participation of the Intelligent Systems Group
(GSI) at Universidad Polit´ecnica de Madrid (UPM) in the Sentiment Analysis workshop
focused in Spanish tweets, TASS2015. This year two challenges have been
proposed, which we have addressed with the design and development of a modular
system that is adaptable to different contexts. This system employs Natural
Language Processing (NLP) and machine-learning technologies, relying also in previously
developed technologies in our research group. In particular, we have used a
wide number of features and polarity lexicons for sentiment detection. With regards
to aspect detection, we have relied on a graph-based algorithm. Once the challenge
has come to an end, the experimental results are promising.",
booktitle = "Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN co-located with 31st SEPLN Conference",
keywords = "Machine Learning, Natural Language Processing, Sentiment analysis, Aspect detection",
month = "September",
pages = "29-34",
title = "{A}spect based {S}entiment {A}nalysis of {S}panish {T}weets",
url = "http://ceur-ws.org/Vol-1397/gsi.pdf",
volume = "1397",
year = "2015",
}
@article{onyx15,
author = "J. Fernando S{\'a}nchez-Rada and Carlos A. Iglesias",
abstract = "Extracting opinions and emotions from text is becoming more and more important, especially since the advent of micro-blogging and social networking.
Opinion mining has become particularly popular and now gathers many public services, datasets and lexical resources.
Unfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications.
Some of the barriers for developing such resources are the diversity of emotion theories and the absence of a common vocabulary to express emotion.
This article presents a semantic vocabulary, called Onyx, intended to provide support to represent emotions in lexical resources and emotion analysis services.
It follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with lemon, an increasingly popular RDF model for representing lexical entries.
This approach also means a new and interesting way to work with different theories of emotion.
As part of our work, Onyx has been aligned with EmotionML and WordNet-Affect.
",
awards = "JCR 1.265 2014 Q2",
comments = "JCR 1.265 2014 Q2",
issn = "0306-4573",
journal = "Information Processing {\&}amp; Management",
keywords = "onyx;emotion analysis;linked data",
month = "January",
pages = "99-114",
title = "{O}nyx: {A} {L}inked {D}ata {A}pproach to {E}motion {R}epresentation",
url = "http://www.sciencedirect.com/science/article/pii/S030645731500045X",
volume = "52",
year = "2016",
}
@incollection{CarlosIglesias16,
author = "Carlos A. Iglesias and J. Fernando S{\'a}nchez-Rada and Gabriela Vulcu and Paul Buitelaar",
booktitle = "Sentiment Analysis in Social Networks",
chapter = "Linked Dat",
isbn = "9780128044124",
keywords = "sentiment analysis;social networks",
month = "October",
pages = "46-66",
publisher = "Morgan Kauffman",
title = "{L}inked {D}ata {M}odels for {S}entiment and {E}motion {A}nalysis in {S}ocial {N}etworks",
url = "http://store.elsevier.com/Sentiment-Analysis-in-Social-Networks/Federico-Alberto-Pozzi/isbn-9780128044124/",
year = "2016",
}
@conference{Sánchez-Rada2016,
author = "J. Fernando S{\'a}nchez-Rada and Carlos A. Iglesias",
booktitle = "Proceedings DSAA 2016 Special Track on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis (SentISData).",
month = "October",
title = "{S}enpy: {A} {P}ragmatic {L}inked {S}entiment {A}nalysis {F}ramework",
year = "2016",
}
@conference{maia14,
author = "S{\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A. and Coronado, Miguel",
abstract = "Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform.",
address = "Warsaw, Poland",
booktitle = "Proceedings of 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology",
keywords = "event, agent architecture, web hook",
month = "August",
organization = "IAT",
publisher = "IEEE Computer Society Press",
title = "{MAIA}: {A}n {E}vent-based {M}odular {A}rchitecture for {I}ntelligent {A}gents",
year = "2014",
}
@conference{eurosentiment-demo,
author = "S{\'a}nchez-Rada, J. Fernando and Gabriela Vulcu and Iglesias, Carlos A. and Paul Buitelaar",
abstract = "Sentiment and Emotion Analysis strongly depend on quality language resources, especially sentiment dictionaries. These resources are usually scattered, heterogeneous and limited to specific domains of application by simple algorithms. The EUROSENTIMENT project addresses these issues by 1) developing a common language resource representation model for sentiment analysis, and APIs for sentiment analysis services based on established Linked Data formats (lemon, Marl, NIF and ONYX) 2) by creating a Language Resource Pool (a.k.a. LRP) that makes available to the community existing scattered language resources and services for sentiment analysis in an interoperable way. In this paper we describe the available language resources and services in the LRP and some sample applications that can be developed on top of the EUROSENTIMENT LRP.",
booktitle = "ISWC 2014 Posters {\&} Demonstrations Track",
editor = "Matthew Horridge, Marco Rospocher, Jacco van Ossenbruggen",
keywords = "eurosentiment",
pages = "145-148",
title = "{EUROSENTIMENT}: {L}inked {D}ata {S}entiment {A}nalysis",
url = "http://ceur-ws.org/Vol-1272/paper_116.pdf",
volume = "1272",
year = "2014",
}
@conference{es3lod14,
author = "Gabriela Vulcu and Paul Buitelaar and Sapna Negi and Bianca Pereira and Mihael Arcan and Barry Coughlan and S{\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A.",
abstract = "We present a methodology for legacy language resource adaptation that generates domain-specific
sentiment lexicons organized around domain entities described with lexical information and
sentiment words described in the context of these entities. We explain the steps of the methodology
and we give a working example of our initial results. The resulting lexicons are modelled as Linked
Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked
sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in
the Linguistic Linked Open Data cloud.
",
address = "Reykjavik, Iceland",
booktitle = "th International Workshop on EMOTION, SOCIAL SIGNALS, SENTIMENT {\&} LINKED OPEN DATA, co-located with LREC 2014,",
keywords = "domain lexion, sentiment analysis",
month = "May",
organization = "LREC2014",
title = "{G}enerating {L}inked-{D}ata based {D}omain-{S}pecific {S}entiment {L}exicons from {L}egacy {L}anguage and {S}emantic {R}esources",
year = "2014",
}
@mastersthesis{clave,
author = "S{\'a}nchez-Rada, J. Fernando",
abstract = "This project aims to introduce an event-based architecture for intelligent agents, inaccordance with the new tendencies in the Evented Web.The reason for this change is that agent communication is no longer suitable for theinmense amount of data generated nowadays and its nature. At least, not for their usein evolving scenarios where data sources interact without previous conguration. This isexactly what the precursors of the Live Web envision, and it is beginning to show in the newgeneration of evented applications, which enable customized interactions and a high level ofcommunication between dierent services.The proposed architecture shown in this document, called Maia, is based on a centralpiece or event router, which controls the ow of information/events to and from the connectedentities. These entities can be either event-aware agents or simply data sources andsubscribers. Thus giving a higher exibility than current technologies and easing the developmentof advanced systems by not requiring the complexity associated with agent systemsin all of the nodes.To demonstrate the feasibility and capabilities of the Maia architecture, a prototype hasbeen implemented which is also explained in detail in this document. It is based on the eventdrivenI/O server side JavaScript environment Node.js for the event routing components,and adapted Jason BDI agent platform as an example of a subscribed multi-agent system.Using this prototype, the benets of using Maia are illustrated by developing a personalagent capable of booking train tickets and that combines access to services, linked data andcommon sense reasoning.",
keywords = "agents, web hooks",
school = "ETSIT-UPM",
title = "{D}esign and {I}mplementation of an {A}gent {A}rchitecture {B}ased on {W}eb {H}ooks",
year = "2012",
}
@conference{ftt14,
author = "S{\'a}nchez-Rada, J. Fernando and Torres, Marcos and Iglesias, Carlos A. and Roberto Maestre and Raquel Peinado",
abstract = "Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities for providers, and complements existing initiatives such as FIBO. The approach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information.",
booktitle = "Second International Workshop on Finance and Economics on the Semantic Web (FEOSW 2014)",
keywords = "linked data, semantic, finance, sentiment analysis, emotions",
title = "{A} {L}inked {D}ata {A}pproach to {S}entiment and {E}motion {A}nalysis of {T}witter in the {F}inancial {D}omain",
url = "http://nadir.uc3m.es/feosw2014/proceedings.html",
year = "2014",
}
@conference{onyx2013,
author = "S{\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A.",
abstract = "There are several different standardised and widespread formats to represent emotions. However, there is no standard semantic model yet. This paper presents a new ontology, called Onyx, that aims to become such a standard while adding concepts from the latest Semantic Web models. In particular, the ontology focuses on the representation of Emotion Analysis results. But the model is abstract and inherits from previous standards and formats. It can thus be used as a reference representation of emotions in any future application or ontology. To prove this, we have translated resources from EmotionML representation to Onyx.
We also present several ways in which developers could benefit from using this ontology instead of an ad-hoc presentation. Our ultimate goal is to foster the use of semantic technologies for emotion Analysis while following the Linked Data ideals.",
address = "Torino, Italy",
booktitle = "Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013)",
month = "December",
organization = "AI*IA, Italian Association for Artificial Intelligence",
pages = "71-82",
publisher = "CEUR-WS",
title = "{O}nyx: {D}escribing {E}motions on the {W}eb of {D}ata",
volume = "1096",
year = "2013",
}
@conference{ldl2013,
author = "Paul Buitelaar and Mihael Arcan and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando and Carlo Strapparava",
abstract = "In this paper we describe the specification of a
model for the semantically interoperable represen-
tation of language resources for sentiment analysis. The model integrates lemon, an RDF-based model for the specification of ontology-lexica
(Buitelaar et al. 2009), which is used increasingly for the representation of language resources as Linked Data, with 'Marl', an RDF-based model for the representation of sentiment annotations (Westerski et al., 2011; S{\'a}nchez-Rada et al., 2013).",
address = "Pisa, Italy",
booktitle = " 2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data. Collocated with the Conference on Generative Approaches to the Lexicon",
editor = "Christian Chiarcos, Philipp Cimiano, Thierry Declerck, John P. McCrae",
keywords = "sentim",
month = "September",
pages = "1-8",
publisher = "Association for Computational Linguistics",
title = "{L}inguistic {L}inked {D}ata for {S}entiment {A}nalysis",
year = "2013",
}
@conference{ATS14,
author = "Jordi Atserias and Marieke van Erp and Isa Maks and Germ{\'a}n Rigau and S{\'a}nchez-Rada, J. Fernando",
address = "Reykjavik, Iceland",
booktitle = "Proceedings of Come Hack with OpeNER!” workshop at the 9th Language Resources and Evaluation Conference (LREC14)",
pages = "5",
publisher = "European Language Resources Association (ELRA)",
title = "{E}uro{L}ove{M}ap: {C}onfronting feelings from {N}ews",
year = "2014",
@article{sanchez-rada_modular_2017,
title = {A modular architecture for intelligent agents in the evented web},
volume = {15},
issn = {1570-1263},
url = {http://content.iospress.com/articles/web-intelligence/web350},
doi = {http://10.3233/WEB-170350},
abstract = {The growing popularity of public APIs and technologies such as web hooks is changing online services drastically. It is easier now than ever to interconnect services and access them as a third party. The next logical step is to use intelligent agents to provide a better user experience across services, connecting services with smart automatic behaviors or actions. In other words, it is time to start using agents in the so-called Evented Web. For this to happen, agent platforms need to seamlessly integrate external sources such as web services. As a solution, this paper introduces an event-based architecture for agent systems. This architecture has been designed in accordance with the new tendencies in web programming and with a Linked Data approach. The use of Linked Data and a specific vocabulary for events allows a smarter and more complex use of events. Two use cases have been implemented to illustrate the validity and usefulness of the architecture.},
number = {1},
journal = {Web Intelligence},
author = {Sánchez-Rada, J. Fernando and Iglesias, Carlos A. and Coronado, Miguel},
month = feb,
year = {2017},
keywords = {web hooks, agent architecture, evented web, events, jason},
pages = {19--33}
}
@inproceedings{araque_aspect_2015,
address = {Alicante, Spain},
title = {Aspect based {Sentiment} {Analysis} of {Spanish} {Tweets}},
volume = {1397},
url = {http://ceur-ws.org/Vol-1397/gsi.pdf},
abstract = {This article presents the participation of the Intelligent Systems Group (GSI) at Universidad Polit´ecnica de Madrid (UPM) in the Sentiment Analysis workshop focused in Spanish tweets, TASS2015. This year two challenges have been proposed, which we have addressed with the design and development of a modular system that is adaptable to different contexts. This system employs Natural Language Processing (NLP) and machine-learning technologies, relying also in previously developed technologies in our research group. In particular, we have used a wide number of features and polarity lexicons for sentiment detection. With regards to aspect detection, we have relied on a graph-based algorithm. Once the challenge has come to an end, the experimental results are promising.},
booktitle = {Proceedings of {TASS} 2015: {Workshop} on {Sentiment} {Analysis} at {SEPLN} co-located with 31st {SEPLN} {Conference} ({SEPLN} 2015)},
publisher = {SEPLN},
author = {Araque, Oscar and Corcuera-Platas, Ignacio and Román-Gómez, Constantino and Iglesias, Carlos A. and Sánchez-Rada, J. Fernando},
month = sep,
year = {2015},
keywords = {aspect detection, Machine Learning, natural language processing, Sentiment analysis, Aspect detection, Natural Language Processing},
pages = {29--34}
}
@inproceedings{sanchez-rada_linked_2015,
address = {Beijing, China},
title = {A {Linked} {Data} {Model} for {Multimodal} {Sentiment} and {Emotion} {Analysis}},
url = {http://aclweb.org/anthology/W/W15/W15-4202.pdf},
abstract = {The number of tools and services for sentiment analysis is increasing rapidly. Unfortunately, the lack of standard formats hinders interoperability. To tackle this problem, previous works propose the use of the NLP Interchange Format (NIF) as both a common semantic format and an API for textual sentiment analysis. However, that approach creates a gap between textual and sentiment analysis that hampers multimodality. This paper presents a multimedia extension of NIF that can be leveraged for multimodal applications. The application of this extended model is illustrated with a service that annotates online videos with their sentiment and the use of SPARQL to retrieve results for different modes.},
publisher = {Association for Computational Linguistics and Asian Federation of Natural Language Processing},
author = {Sánchez-Rada, J. Fernando and Iglesias, Carlos A. and Gil, Ronald},
month = jul,
year = {2015},
keywords = {iglesias, sanchezrada},
pages = {11--19}
}
@inproceedings{araque_applying_2017,
title = {Applying {Recurrent} {Neural} {Networks} to {Sentiment} {Analysis} of {Spanish} {Tweets}},
volume = {1896},
url = {http://ceur-ws.org/Vol-1896/p8_gsi_tass2017.pdf},
abstract = {This article presents the participation of the Intelligent Systems Group (GSI) at Universidad Polit ́ecnica de Madrid (UPM) in the Sentiment Analysis work- shop focused in Spanish tweets, TASS2017. We have worked on Task 1, aiming to classify sentiment polarity of Spanish tweets. For this task we propose a Recurrent Neural Network (RNN) architecture composed of Long Short-Term Memory (LSTM) cells followed by a feedforward network. The architecture makes use of two different types of features: word embeddings and sentiment lexicon values. The recurrent ar- chitecture allows us to process text sequences of different lengths, while the lexicon inserts directly into the system sentiment information. The results indicate that this feature combination leads to enhanced sentiment analysis performances.},
author = {Araque, Oscar and Barbado, Rodrigo and Sánchez-Rada, J. Fernando and Iglesias, Carlos A.},
editor = {WS, Ceur},
year = {2017},
keywords = {sentiment analysis, deep learning, natural language processing, recurrent neural networks}
}
@article{araque_enhancing_2017,
title = {Enhancing {Deep} {Learning} {Sentiment} {Analysis} with {Ensemble} {Techniques} in {Social} {Applications}},
issn = {0957-4174},
url = {http://www.sciencedirect.com/science/article/pii/S0957417417300751},
doi = {http://dx.doi.org/10.1016/j.eswa.2017.02.002},
abstract = {The appearance of new Deep Learning applications for Sentiment Analysis has motivated a lot of researchers, mainly because of their automatic feature extraction and representation capabilities, as well as their better performance compared to the previous feature based techniques. These traditional surface approaches are based on complex manually extracted features, and this extraction process is a fundamental question in feature driven methods. However, these long-established approaches can yield strong baselines on their own, and its predictive capabilities can be used in conjunction with the arising Deep Learning methods. In this paper we seek to improve the performance of these new Deep Learning techniques integrating them with more traditional surface approaches based on manually extracted features. The contributions of this paper are: first, we develop a Deep Learning based Sentiment classifier using the Word2Vec model and a linear machine learning algorithm. This classifier serves us as a baseline with which we can compare subsequent results. Second, we propose two ensemble techniques which aggregate our baseline classifier with other surface classifiers widely used in the field of Sentiment Analysis. Third, we also propose two models for combining deep features with both surface and deep features in order to merge the information from several sources. As fourth contribution, we introduce a taxonomy for classifying the different models we propose, as well as the ones found in the literature. Fifth, we conduct several reproducible experiments with the aim of comparing the performance of these models with the Deep Learning baseline. For this, we employ four public datasets that were extracted from the microblogging domain. Finally, as a result, the experiments confirm that the performance of these proposed models surpasses that of our original baseline using as metric the F1-Score, with improvements ranging from 0.21 to 3.62 \%.},
journal = {Expert Systems with Applications},
author = {Araque, Oscar and Corcuera-Platas, Ignacio and Sánchez-Rada, J. Fernando and Iglesias, Carlos A.},
month = jun,
year = {2017},
keywords = {sentiment analysis, deep learning, Machine learning, Ensemble, Deep learning, Natural language processing, natural language processing, Sentiment analysis, downstream ensemble, machine learning},
file = {Araque et al_2017_Enhancing deep learning sentiment analysis with ensemble techniques in social.pdf:/home/j/.zotero/zotero/sv7qncga.default/zotero/storage/7MMJ8GHM/Araque et al_2017_Enhancing deep learning sentiment analysis with ensemble techniques in social.pdf:application/pdf;ScienceDirect Snapshot:/home/j/.zotero/zotero/sv7qncga.default/zotero/storage/6ISKPHVB/S0957417417300751.html:text/html}
}
@inproceedings{atserias_eurolovemap:_2014,
address = {Reykjavik, Iceland},
title = {{EuroLoveMap}: {Confronting} feelings from {News}},
isbn = {978-2-9517408-8-4},
booktitle = {Proceedings of {Come} {Hack} with {OpeNER}!” workshop at the 9th {Language} {Resources} and {Evaluation} {Conference} ({LREC}14)},
publisher = {European Language Resources Association (ELRA)},
author = {Atserias, Jordi and Erp, Marieke van and Maks, Isa and Rigau, Germán and Sánchez-Rada, J. Fernando},
year = {2014},
pages = {5}
}
@inproceedings{sanchez-rada_eurosentiment:_2014,
address = {Riva del Garda, Trentino},
title = {{EUROSENTIMENT}: {Linked} {Data} {Sentiment} {Analysis}},
volume = {1272},
url = {http://ceur-ws.org/Vol-1272/paper_116.pdf},
abstract = {Sentiment and Emotion Analysis strongly depend on quality language resources, especially sentiment dictionaries. These resources are usually scattered, heterogeneous and limited to specific domains of application by simple algorithms. The EUROSENTIMENT project addresses these issues by 1) developing a common language resource representation model for sentiment analysis, and APIs for sentiment analysis services based on established Linked Data formats (lemon, Marl, NIF and ONYX) 2) by creating a Language Resource Pool (a.k.a. LRP) that makes available to the community existing scattered language resources and services for sentiment analysis in an interoperable way. In this paper we describe the available language resources and services in the LRP and some sample applications that can be developed on top of the EUROSENTIMENT LRP.},
booktitle = {Proceedings of the {ISWC} 2014 {Posters} \& {Demonstrations} {Track} a track within the 13th {International} {Semantic} {Web} {Conference} ({ISWC} 2014)},
publisher = {ISWC},
author = {Sánchez-Rada, J. Fernando and Vulcu, Gabriela and Iglesias, Carlos A. and Buitelaar, Paul},
editor = {Matthew Horridge, Marco Rospocher, Jacco van Ossenbruggen},
month = oct,
year = {2014},
keywords = {eurosentiment},
pages = {145--148}
}
@inproceedings{sanchez-rada_linked_2014,
title = {A {Linked} {Data} {Approach} to {Sentiment} and {Emotion} {Analysis} of {Twitter} in the {Financial} {Domain}},
volume = {1240},
url = {http://ceur-ws.org/Vol-1240/},
abstract = {Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities for providers, and complements existing initiatives such as FIBO. The approach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information.},
booktitle = {Second {International} {Workshop} on {Finance} and {Economics} on the {Semantic} {Web} ({FEOSW} 2014)},
author = {Sánchez-Rada, J. Fernando and Torres, Marcos and Iglesias, Carlos A. and Maestre, Roberto and Peinado, Raquel},
month = may,
year = {2014},
keywords = {emotions, sentiment analysis, finance, linked data, semantic},
pages = {51--62}
}
@inproceedings{vulcu_generating_2014,
address = {Reykjavik, Iceland},
title = {Generating {Linked}-{Data} based {Domain}-{Specific} {Sentiment} {Lexicons} from {Legacy} {Language} and {Semantic} {Resources}},
abstract = {We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud.},
booktitle = {th {International} {Workshop} on {Emotion}, {Social} {Signals}, {Sentiment} \& {Linked} {Open} {Data}, co-located with {LREC} 2014,},
publisher = {LREC2014},
author = {Vulcu, Gabriela and Buitelaar, Paul and Negi, Sapna and Pereira, Bianca and Arcan, Mihael and Coughlan, Barry and Sánchez-Rada, J. Fernando and Iglesias, Carlos A.},
month = may,
year = {2014},
keywords = {sentiment analysis, Sentiment analysis, domain lexicon, domain lexion},
pages = {6--9}
}
@inproceedings{buitelaar_linguistic_2013,
address = {Pisa, Italy},
title = {Linguistic {Linked} {Data} for {Sentiment} {Analysis}},
isbn = {978-1-937284-77-0},
url = {https://www.aclweb.org/anthology/W/W13/W13-55.pdf},
abstract = {In this paper we describe the specification of a model for the semantically interoperable represen- tation of language resources for sentiment analysis. The model integrates lemon, an RDF-based model for the specification of ontology-lexica (Buitelaar et al. 2009), which is used increasingly for the representation of language resources as Linked Data, with 'Marl', an RDF-based model for the representation of sentiment annotations (Westerski et al., 2011; Sánchez-Rada et al., 2013).},
booktitle = {2nd {Workshop} on {Linked} {Data} in {Linguistics} ({LDL}-2013): {Representing} and linking lexicons, terminologies and other language data. {Collocated} with the {Conference} on {Generative} {Approaches} to the {Lexicon}},
publisher = {Association for Computational Linguistics},
author = {Buitelaar, Paul and Arcan, Mihael and Iglesias, Carlos A. and Sánchez-Rada, J. Fernando and Strapparava, Carlo},
editor = {Christian Chiarcos, Philipp Cimiano, Thierry Declerck, John P. McCrae},
month = sep,
year = {2013},
keywords = {sentim},
pages = {1--8}
}
@inproceedings{sanchez-rada_towards_2016,
title = {Towards a {Common} {Linked} {Data} {Model} for {Sentiment} and {Emotion} {Analysis}},
url = {http://www.lrec-conf.org/proceedings/lrec2016/workshops/LREC2016Workshop-ESA_Proceedings.pdf},
abstract = {The different formats to encode information currently in use in sentiment analysis and opinion mining are heterogeneous and often custom tailored to each application. Besides a number of existing standards, there are additionally still plenty of open challenges, such as representing sentiment and emotion in web services, integration of different models of emotions or linking to other data sources. In this paper, we motivate the switch to a linked data approach in sentiment and emotion analysis that would overcome these and other current limitations. This paper includes a review of the existing approaches and their limitations, an introduction of the elements that would make this change possible, and a discussion of the challenges behind that change.},
booktitle = {Proceedings of the {LREC} 2016 {Workshop} {Emotion} and {Sentiment} {Analysis} ({ESA} 2016)},
publisher = {LREC 2016},
author = {Sánchez-Rada, J. Fernando and Schuller, Björn and Patti, Viviana and Buitelaar, Paul and Vulcu, Gabriela and Bulkhardt, Felix and Clavel, Chloé and Petychakis, Michael and Iglesias, Carlos A.},
editor = {Sánchez-Rada, J. Fernando and Schuller, Björn},
month = may,
year = {2016},
keywords = {emotion, linked data, sentiment},
pages = {48--54}
}
@article{buitelaar_mixedemotions:_2018,
title = {{MixedEmotions}: {An} {Open}-{Source} {Toolbox} for {Multi}-{Modal} {Emotion} {Analysis}},
issn = {1520-9210},
url = {http://ieeexplore.ieee.org/document/8269329/},
abstract = {Recently, there is an increasing tendency to embed the functionality of recognizing emotions from the user generated contents, to infer richer profile about the users or contents, that can be used for various automated systems such as call-center operations, recommendations, and assistive technologies. However, to date, adding this functionality was a tedious, costly, and time consuming effort, and one should look for different tools that suits one's needs, and should provide different interfaces to use those tools. The MixedEmotions toolbox leverages the need for such functionalities by providing tools for text, audio, video, and linked data processing within an easily integrable plug-and-play platform. These functionalities include: (i) for text processing: emotion and sentiment recognition, (ii) for audio processing: emotion, age, and gender recognition, (iii) for video processing: face detection and tracking, emotion recognition, facial landmark localization, head pose estimation, face alignment, and body pose estimation, and (iv) for linked data: knowledge graph. Moreover, the MixedEmotions Toolbox is open-source and free. In this article, we present this toolbox in the context of the existing landscape, and provide a range of detailed benchmarks on standardized test-beds showing its state-of-the-art performance. Furthermore, three real-world use-cases show its effectiveness, namely emotion-driven smart TV, call center monitoring, and brand reputation analysis.},
journal = {IEEE Transactions on Multimedia},
author = {Buitelaar, Paul and Wood, Ian D. and Arcan, Mihael and McCrae, John P. and Abele, Andrejs and Robin, Cécile and Andryushechkin, Vladimir and Ziad, Housam and Sagha, Hesam and Schmitt, Maximilian and Schuller, Björn W. and Sánchez-Rada, J. Fernando and Iglesias, Carlos A. and Navarro, Carlos and Giefer, Andreas and Heise, Nicolaus and Masucci, Vincenzo and Danza, Francesco A. and Caterino, Ciro and Smrž, Pavel and Hradiš, Michal and Povolný, Filip and Klimeš, Marek and Matějka, Pavel and Tummarello, Giovanni},
month = oct,
year = {2018},
keywords = {affective computing, linked data, audio processing, emotion analysi, open source toolbox, text processing, video processing}
}
@incollection{merino_modeling_2017,
title = {Modeling {Social} {Influence} in {Social} {Networks} with {SOIL}, a {Python} {Agent}-{Based} {Social} {Simulator}},
volume = {10349},
isbn = {978-3-319-59929-8},
url = {https://link.springer.com/chapter/10.1007/978-3-319-59930-4_33},
abstract = {The application of Agent-based Social Simulation (ABSS) for modeling social networks requires specific facilities for modeling, simulation and visualization of network structures. Moreover, ABSS can benefit from interactive shell facilities that can assist the model development process. We have addressed these problems through the development of a tool called SOIL, which provides a Python ABSS specifically designed for social networks. In this paper we present how this tool is applied to simulate viral marketing processes in a social network, and to evaluate the model with real data.},
booktitle = {Advances in {Practical} {Applications} of {Cyber}-{Physical} {Multi}-{Agent} {Systems}: {The} {PAAMS} {Collection}},
publisher = {Springer-Verlag},
author = {Merino, Eduardo and Sánchez, Jesús M. and García Martín, David and Sánchez-Rada, J. Fernando and Iglesias, Carlos A.},
editor = {Demazeau Y., Davidsson P., Bajo J., Vale Z.},
month = jun,
year = {2017},
keywords = {social network, agent based social simulation, networkx, soil},
pages = {337--341}
}
@inproceedings{sanchez-rada_multimodal_2017,
address = {San Antonio, Texas, USA},
title = {Multimodal {Multimodel} {Emotion} {Analysis} as {Linked} {Data}},
abstract = {The lack of a standard emotion representation model hinders emotion analysis due to the incompatibility of annota-tion formats and models from different sources, tools and an- notation services. This is also a limiting factor for multimodal analysis, since recognition services from different modalities (audio, video, text) tend to have different representation models (e. g., continuous vs. discrete emotions). This work presents a multi-disciplinary effort to alleviate this problem by formalizing conversion between emotion models. The specific contributions are: i) a semantic representation of emotion conversion; ii) an API proposal for services that perform automatic conversion; iii) a reference implementation of such a service; and iv) validation of the proposal through use cases that integrate different emotion models and service providers.},
booktitle = {Proceedings of {ACII} 2017},
author = {Sánchez-Rada, J. Fernando and Iglesias, Carlos A. and Sagha, Hesam and Schuller, Björn and Wood, Ian and Buitelaar, Paul},
month = oct,
year = {2017},
keywords = {social networks, emotion analysis, linked data}
}
@article{sanchez-rada_onyx:_2016,
title = {Onyx: {A} {Linked} {Data} {Approach} to {Emotion} {Representation}},
volume = {52},
issn = {0306-4573},
url = {http://www.sciencedirect.com/science/article/pii/S030645731500045X},
abstract = {Extracting opinions and emotions from text is becoming more and more important, especially since the advent of micro-blogging and social networking. Opinion mining has become particularly popular and now gathers many public services, datasets and lexical resources. Unfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications. Some of the barriers for developing such resources are the diversity of emotion theories and the absence of a common vocabulary to express emotion. This article presents a semantic vocabulary, called Onyx, intended to provide support to represent emotions in lexical resources and emotion analysis services. It follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with lemon, an increasingly popular RDF model for representing lexical entries. This approach also means a new and interesting way to work with different theories of emotion. As part of our work, Onyx has been aligned with EmotionML and WordNet-Affect.},
journal = {Information Processing \& Management},
author = {Sánchez-Rada, J. Fernando and Iglesias, Carlos A.},
month = jan,
year = {2016},
keywords = {onyx, Linked data, EmotionML, emotion analysis, Emotionml, Linked Data, Ontology, Provenance, Lexical resources, linked data},
pages = {99--114},
file = {ScienceDirect Full Text PDF:/home/j/.zotero/zotero/sv7qncga.default/zotero/storage/8IMGG77X/Sánchez-Rada and Iglesias - 2016 - Onyx A Linked Data approach to emotion representa.pdf:application/pdf;ScienceDirect Snapshot:/home/j/.zotero/zotero/sv7qncga.default/zotero/storage/KWN6QVT2/S030645731500045X.html:text/html}
}
@inproceedings{sanchez-rada_onyx:_2013,
address = {Torino, Italy},
title = {Onyx: {Describing} {Emotions} on the {Web} of {Data}},
volume = {1096},
abstract = {There are several different standardised and widespread formats to represent emotions. However, there is no standard semantic model yet. This paper presents a new ontology, called Onyx, that aims to become such a standard while adding concepts from the latest Semantic Web models. In particular, the ontology focuses on the representation of Emotion Analysis results. But the model is abstract and inherits from previous standards and formats. It can thus be used as a reference representation of emotions in any future application or ontology. To prove this, we have translated resources from EmotionML representation to Onyx. We also present several ways in which developers could benefit from using this ontology instead of an ad-hoc presentation. Our ultimate goal is to foster the use of semantic technologies for emotion Analysis while following the Linked Data ideals.},
booktitle = {Proceedings of the {First} {International} {Workshop} on {Emotion} and {Sentiment} in {Social} and {Expressive} {Media}: approaches and perspectives from {AI} ({ESSEM} 2013)},
publisher = {CEUR-WS},
author = {Sánchez-Rada, J. Fernando and Iglesias, Carlos A.},
month = dec,
year = {2013},
keywords = {emotions, semantic web, sentiment analysis, emotion analysis, Emotionml, Lemon, Linked Data, Ontology, Provenance, Semantic},
pages = {71--82}
}
@inproceedings{sanchez-rada_senpy:_2016,
address = {Montreal, Canada},
title = {Senpy: {A} {Pragmatic} {Linked} {Sentiment} {Analysis} {Framework}},
url = {http://ieeexplore.ieee.org/abstract/document/7796961/},
abstract = {Sentiment and emotion analysis technologies have quickly gained momentum in industry and academia. This popularity has spawned a myriad of service and tools. Due to the lack of common interfaces and models, each of these services imposes specific interfaces and representation models. Heterogeneity makes it costly to integrate different services, evaluate them or switch between them. This work aims to remedy heterogeneity by providing an extensible framework and an API aligned with the NLP Interchange Format service specification. It also includes a reference implementation, a first step towards a successful and cost-effective adoption. The specific contributions in this paper are: (i) the Senpy framework; (ii) an architecture for the framework that follows a plug-in approach; (iii) a reference open source implementation of the architecture; (iv) the use and validation of the framework and architecture in a big data sentiment analysis European project. Our aim is to foster the development of a new generation of emotion aware services by isolating the development of new algorithms from the representation of results and the deployment of services.},
booktitle = {Proceedings {DSAA} 2016 {Special} {Track} on {Emotion} and {Sentiment} in {Intelligent} {Systems} and {Big} {Social} {Data} {Analysis} ({SentISData})},
publisher = {IEEE},
author = {Sánchez-Rada, J. Fernando and Iglesias, Carlos A. and Corcuera-Platas, Ignacio and Araque, Oscar},
month = oct,
year = {2016},
keywords = {sentiment analysis, emotion analysis, framework},
pages = {735--742}
}
@incollection{iglesias_linked_2016,
title = {Linked {Data} {Models} for {Sentiment} and {Emotion} {Analysis} in {Social} {Networks}},
isbn = {978-0-12-804412-4},
url = {http://store.elsevier.com/Sentiment-Analysis-in-Social-Networks/Federico-Alberto-Pozzi/isbn-9780128044124/},
abstract = {Language resource interoperability is still a major challenge in sentiment analysis. One of the current trends for solving this issue is the adoption of a linked data perspective for semantically modeling, interlinking, and publishing lexical and other linguistic resources. This chapter contributes to the development of the linguistic linked open data through a linked data model for sentiment and emotion analysis in social networks that is based on two vocabularies: Marl and Onyx for sentiment and emotion modeling respectively. These vocabularies are used for (1) affective corpus annotation, (2) affective lexicon annotation, and (3) sentiment and emotion services interoperability. Several aspects of the solution are discussed, such as the transformation of legacy resources, the generation of domain-specific sentiment lexicons, and the benefits of interlinking language resources for sentiment analysis with other resources such as WordNet or DBpedia.},
booktitle = {Sentiment {Analysis} in {Social} {Networks}},
publisher = {Morgan Kauffman},
author = {Iglesias, Carlos A. and Sánchez-Rada, J. Fernando and Vulcu, Gabriela and Buitelaar, Paul},
editor = {Pozzi, Federico Alberto and Fersini, Elisabetta and Messina, Enza and Liu, Bing},
month = oct,
year = {2016},
keywords = {sentiment analysis, social networks},
pages = {46--66}
}
@incollection{sanchez_soil:_2017,
series = {{LNAI}},
title = {Soil: {An} {Agent}-{Based} {Social} {Simulator} in {Python} for {Modelling} and {Simulation} of {Social} {Networks}},
volume = {10349},
isbn = {978-3-319-59929-8},
url = {https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19},
abstract = {Social networks have a great impact in our lives. While they started to improve and aid communication, nowadays they are used both in professional and personal spheres, and their popularity has made them attractive for developing a number of business models. Agent-based Social Simulation (ABSS) is one of the techniques that has been used for analysing and simulating social networks with the aim of understanding and even forecasting their dynamics. Nevertheless, most available ABSS platforms do not provide specific facilities for modelling, simulating and visualising social networks. This article aims at bridging this gap by introducing an ABSS platform specifically designed for modelling social networks. The main contributions of this paper are: (1) a review and characterisation of existing ABSS platforms; (2) the design of an ABSS platform for social network modelling and simulation; and (3) the development of a number of behaviour models for evaluating the platform for information, rumours and emotion propagation. Finally, the article is complemented by a free and open source simulator.},
booktitle = {Advances in {Practical} {Applications} of {Cyber}-{Physical} {Multi}-{Agent} {Systems}: {The} {PAAMS} {Collection}},
publisher = {Springer Verlag},
author = {Sánchez, Jesús M. and Iglesias, Carlos A. and Sánchez-Rada, J. Fernando},
editor = {Demazeau Y., Davidsson P., Bajo J., Vale Z.},
month = jun,
year = {2017},
note = {DOI: 10.1007/978-3-319-59930-4\_19},
keywords = {social networks, agent based social simulation, soil, python},
pages = {234--245}
}
@inproceedings{sanchez-rada_maia:_2014,
address = {Warsaw, Poland},
title = {{MAIA}: {An} {Event}-based {Modular} {Architecture} for {Intelligent} {Agents}},
abstract = {Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform.},
booktitle = {Proceedings of 2014 {IEEE}/{WIC}/{ACM} {International} {Conference} on {Intelligent} {Agent} {Technology}},
publisher = {IEEE Computer Society Press},
author = {Sánchez-Rada, J Fernando and Iglesias, Carlos A and Coronado, Miguel},
month = aug,
year = {2014},
keywords = {agent architecture, event, maia, web hook}
}
@mastersthesis{sanchez-rada_design_2012,
title = {Design and {Implementation} of an {Agent} {Architecture} {Based} on {Web} {Hooks}},
abstract = {This project aims to introduce an event-based architecture for intelligent agents, inaccordance with the new tendencies in the Evented Web.The reason for this change is that agent communication is no longer suitable for theinmense amount of data generated nowadays and its nature. At least, not for their usein evolving scenarios where data sources interact without previous con guration. This isexactly what the precursors of the Live Web envision, and it is beginning to show in the newgeneration of evented applications, which enable customized interactions and a high level ofcommunication between di erent services.The proposed architecture shown in this document, called Maia, is based on a centralpiece or event router, which controls the ow of information/events to and from the connectedentities. These entities can be either event-aware agents or simply data sources andsubscribers. Thus giving a higher exibility than current technologies and easing the developmentof advanced systems by not requiring the complexity associated with agent systemsin all of the nodes.To demonstrate the feasibility and capabilities of the Maia architecture, a prototype hasbeen implemented which is also explained in detail in this document. It is based on the eventdrivenI/O server side JavaScript environment Node.js for the event routing components,and adapted Jason BDI agent platform as an example of a subscribed multi-agent system.Using this prototype, the bene ts of using Maia are illustrated by developing a personalagent capable of booking train tickets and that combines access to services, linked data andcommon sense reasoning.},
school = {ETSIT-UPM},
author = {Sánchez-Rada, J Fernando},
year = {2012},
keywords = {agents, web hooks}
}

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"summary": "The Ingelligent Systems Group is a research group at Universidad Politécnica de Madrid (UPM)",
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@ -115,23 +115,72 @@
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"name": "An Event-based Modular Architecture for Intelligent Agents",
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{
"publications": [
{
"authors": "J. Fernando S{\\'a}nchez-Rada and Carlos A. Iglesias",
"releaseDate": "2016",
"summary": "Extracting opinions and emotions from text is becoming more and more important, especially since the advent of micro-blogging and social networking.\nOpinion mining has become particularly popular and now gathers many public services, datasets and lexical resources.\nUnfortunately, there are few available lexical and semantic resources for emotion recognition that could foster the development of new emotion aware services and applications.\nSome of the barriers for developing such resources are the diversity of emotion theories and the absence of a common vocabulary to express emotion.\nThis article presents a semantic vocabulary, called Onyx, intended to provide support to represent emotions in lexical resources and emotion analysis services.\nIt follows a linguistic Linked Data approach, it is aligned with the Provenance Ontology, and it has been integrated with lemon, an increasingly popular RDF model for representing lexical entries.\nThis approach also means a new and interesting way to work with different theories of emotion.\nAs part of our work, Onyx has been aligned with EmotionML and WordNet-Affect.",
"name": "Onyx: A Linked Data Approach to Emotion Representation"
},
{
"authors": "Carlos A. Iglesias and J. Fernando S{\\'a}nchez-Rada and Gabriela Vulcu and Paul Buitelaar",
"publisher": "Sentiment Analysis in Social Networks",
"releaseDate": "2016",
"name": "Linked Data Models for Sentiment and Emotion Analysis in Social Networks"
},
{
"authors": "J. Fernando S{\\'a}nchez-Rada and Carlos A. Iglesias",
"publisher": "Proceedings DSAA 2016 Special Track on Emotion and Sentiment in Intelligent Systems and Big Social Data Analysis (SentISData).",
"releaseDate": "2016",
"name": "Senpy: A Pragmatic Linked Sentiment Analysis Framework"
},
{
"authors": "Oscar Araque and Ignacio Corcuera-Platas and Constantino Rom{\\'a}n-G{\\'o}mez and Carlos A. Iglesias and J. Fernando S{\\'a}nchez-Rada",
"publisher": "Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN co-located with 31st SEPLN Conference",
"releaseDate": "2015",
"summary": "This article presents the participation of the Intelligent Systems Group\n(GSI) at Universidad Polit\u00b4ecnica de Madrid (UPM) in the Sentiment Analysis workshop\nfocused in Spanish tweets, TASS2015. This year two challenges have been\nproposed, which we have addressed with the design and development of a modular\nsystem that is adaptable to different contexts. This system employs Natural\nLanguage Processing (NLP) and machine-learning technologies, relying also in previously\ndeveloped technologies in our research group. In particular, we have used a\nwide number of features and polarity lexicons for sentiment detection. With regards\nto aspect detection, we have relied on a graph-based algorithm. Once the challenge\nhas come to an end, the experimental results are promising.",
"name": "Aspect based Sentiment Analysis of Spanish Tweets"
},
{
"authors": "J. Fernando S{\\'a}nchez-Rada and Carlos A. Iglesias and Ronald Gil",
"releaseDate": "2015",
"name": "A Linked Data Model for Multimodal Sentiment and Emotion Analysis"
},
{
"authors": "J. Fernando S{\\'a}nchez-Rada and Carlos A. Iglesias and Ronald Gil",
"releaseDate": "2015",
"name": "A Linked Data Model for Multimodal Sentiment and Emotion Analysis"
},
{
"authors": "Oscar Araque and Ignacio Corcuera-Platas and Constantino Rom{\\'a}n-G{\\'o}mez and Carlos A. Iglesias and J. Fernando S{\\'a}nchez-Rada",
"publisher": "Proceedings of TASS 2015: Workshop on Sentiment Analysis at SEPLN co-located with 31st SEPLN Conference",
"releaseDate": "2015",
"summary": "This article presents the participation of the Intelligent Systems Group\n(GSI) at Universidad Polit\u00b4ecnica de Madrid (UPM) in the Sentiment Analysis workshop\nfocused in Spanish tweets, TASS2015. This year two challenges have been\nproposed, which we have addressed with the design and development of a modular\nsystem that is adaptable to different contexts. This system employs Natural\nLanguage Processing (NLP) and machine-learning technologies, relying also in previously\ndeveloped technologies in our research group. In particular, we have used a\nwide number of features and polarity lexicons for sentiment detection. With regards\nto aspect detection, we have relied on a graph-based algorithm. Once the challenge\nhas come to an end, the experimental results are promising.",
"name": "Aspect based Sentiment Analysis of Spanish Tweets"
},
{
"authors": "S{\\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A. and Coronado, Miguel",
"publisher": "Proceedings of 2014 IEEE/WIC/ACM International Conference on Intelligent Agent Technology",
"releaseDate": "2014",
"summary": "Online services are no longer isolated. The release of public APIs and technologies such as web hooks are allowing users and developers to access their information easily. Intelligent agents could use this information to provide a better user experience across services, connecting services with smart automatic behaviours or actions. However, agent platforms are not prepared to easily add external sources such as web services, which hinders the usage of agents in the so-called Evented or Live Web. As a solution, this paper introduces an event-based architecture for agent systems, in accordance with the new tendencies in web programming. In particular, it is focused on personal agents that interact with several web services. With this architecture, called MAIA, connecting to new web services does not involve any modification in the platform.",
"name": "MAIA: An Event-based Modular Architecture for Intelligent Agents"
},
{
"authors": "S{\\'a}nchez-Rada, J. Fernando and Gabriela Vulcu and Iglesias, Carlos A. and Paul Buitelaar",
"publisher": "ISWC 2014 Posters {\\&} Demonstrations Track",
"releaseDate": "2014",
"summary": "Sentiment and Emotion Analysis strongly depend on quality language resources, especially sentiment dictionaries. These resources are usually scattered, heterogeneous and limited to specific domains of application by simple algorithms. The EUROSENTIMENT project addresses these issues by 1) developing a common language resource representation model for sentiment analysis, and APIs for sentiment analysis services based on established Linked Data formats (lemon, Marl, NIF and ONYX) 2) by creating a Language Resource Pool (a.k.a. LRP) that makes available to the community existing scattered language resources and services for sentiment analysis in an interoperable way. In this paper we describe the available language resources and services in the LRP and some sample applications that can be developed on top of the EUROSENTIMENT LRP.",
"name": "EUROSENTIMENT: Linked Data Sentiment Analysis"
},
{
"authors": "Gabriela Vulcu and Paul Buitelaar and Sapna Negi and Bianca Pereira and Mihael Arcan and Barry Coughlan and S{\\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A.",
"publisher": "th International Workshop on EMOTION, SOCIAL SIGNALS, SENTIMENT {\\&} LINKED OPEN DATA, co-located with LREC 2014,",
"releaseDate": "2014",
"summary": "We present a methodology for legacy language resource adaptation that generates domain-specific\nsentiment lexicons organized around domain entities described with lexical information and\nsentiment words described in the context of these entities. We explain the steps of the methodology\nand we give a working example of our initial results. The resulting lexicons are modelled as Linked\nData resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked\nsentiment expressions (Marl), thereby contributing and linking to existing Language Resources in\nthe Linguistic Linked Open Data cloud.",
"name": "Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources"
},
{
"authors": "S{\\'a}nchez-Rada, J. Fernando and Torres, Marcos and Iglesias, Carlos A. and Roberto Maestre and Raquel Peinado",
"publisher": "Second International Workshop on Finance and Economics on the Semantic Web (FEOSW 2014)",
"releaseDate": "2014",
"summary": "Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked data approach for modelling sentiment and emotions about financial entities. We aim at integrating sentiment information from different communities for providers, and complements existing initiatives such as FIBO. The approach has been validated in the semantic annotation of tweets of several stocks in the Spanish stock market, including its sentiment information.",
"name": "A Linked Data Approach to Sentiment and Emotion Analysis of Twitter in the Financial Domain"
},
{
"authors": "Jordi Atserias and Marieke van Erp and Isa Maks and Germ{\\'a}n Rigau and S{\\'a}nchez-Rada, J. Fernando",
"publisher": "Proceedings of Come Hack with OpeNER!\u201d workshop at the 9th Language Resources and Evaluation Conference (LREC\u201914)",
"releaseDate": "2014",
"name": "EuroLoveMap: Confronting feelings from News"
},
{
"authors": "S{\\'a}nchez-Rada, J. Fernando and Iglesias, Carlos A.",
"publisher": "Proceedings of the First International Workshop on Emotion and Sentiment in Social and Expressive Media: approaches and perspectives from AI (ESSEM 2013)",
"releaseDate": "2013",
"summary": "There are several different standardised and widespread formats to represent emotions. However, there is no standard semantic model yet. This paper presents a new ontology, called Onyx, that aims to become such a standard while adding concepts from the latest Semantic Web models. In particular, the ontology focuses on the representation of Emotion Analysis results. But the model is abstract and inherits from previous standards and formats. It can thus be used as a reference representation of emotions in any future application or ontology. To prove this, we have translated resources from EmotionML representation to Onyx.\nWe also present several ways in which developers could benefit from using this ontology instead of an ad-hoc presentation. Our ultimate goal is to foster the use of semantic technologies for emotion Analysis while following the Linked Data ideals.",
"name": "Onyx: Describing Emotions on the Web of Data"
},
{
"authors": "Paul Buitelaar and Mihael Arcan and Iglesias, Carlos A. and S{\\'a}nchez-Rada, J. Fernando and Carlo Strapparava",
"publisher": "2nd Workshop on Linked Data in Linguistics (LDL-2013): Representing and linking lexicons, terminologies and other language data. Collocated with the Conference on Generative Approaches to the Lexicon",
"releaseDate": "2013",
"summary": "In this paper we describe the specification of a\nmodel for the semantically interoperable represen-\ntation of language resources for sentiment analysis. The model integrates \u2018lemon\u2019, an RDF-based model for the specification of ontology-lexica\n(Buitelaar et al. 2009), which is used increasingly for the representation of language resources as Linked Data, with 'Marl', an RDF-based model for the representation of sentiment annotations (Westerski et al., 2011; S{\\'a}nchez-Rada et al., 2013).",
"name": "Linguistic Linked Data for Sentiment Analysis"
},
{
"authors": "S{\\'a}nchez-Rada, J. Fernando",
"releaseDate": "2012",
"summary": "This project aims to introduce an event-based architecture for intelligent agents, inaccordance with the new tendencies in the Evented Web.The reason for this change is that agent communication is no longer suitable for theinmense amount of data generated nowadays and its nature. At least, not for their usein evolving scenarios where data sources interact without previous con\u001cguration. This isexactly what the precursors of the Live Web envision, and it is beginning to show in the newgeneration of evented applications, which enable customized interactions and a high level ofcommunication between di\u001berent services.The proposed architecture shown in this document, called Maia, is based on a centralpiece or event router, which controls the \u001dow of information/events to and from the connectedentities. These entities can be either event-aware agents or simply data sources andsubscribers. Thus giving a higher \u001dexibility than current technologies and easing the developmentof advanced systems by not requiring the complexity associated with agent systemsin all of the nodes.To demonstrate the feasibility and capabilities of the Maia architecture, a prototype hasbeen implemented which is also explained in detail in this document. It is based on the eventdrivenI/O server side JavaScript environment Node.js for the event routing components,and adapted Jason BDI agent platform as an example of a subscribed multi-agent system.Using this prototype, the bene\u001cts of using Maia are illustrated by developing a personalagent capable of booking train tickets and that combines access to services, linked data andcommon sense reasoning.",
"name": "Design and Implementation of an Agent Architecture Based on Web Hooks"
}
],
"interests": [],
"volunteer": [
{
"endDate": "2012-05-01",
"website": "http://eestec.net/",
"summary": "",
"organization": "EESTEC",
"startDate": "2011-04-01",
"position": "Vice-Chairman for External Affairs",
"highlights": [
"Established connections with other Student Associations",
"Helped found new Observers (Local Groups)",
"Carried out International Board duties"
]
},
{
"endDate": "2013-05-01",
"website": "http://eestec.net",
"summary": "",
"organization": "EESTEC",
"startDate": "2012-04-01",
"position": "IT Coordinator",
"highlights": [
"Coordinated the work of a small international IT Team",
"In charge of the administration of the IT infrastructure of EESTEC: Plone portal, Mailman mailing lists, etc."
]
},
{
"endDate": "2013-05-01",
"website": "http://eestec.net",
"summary": "",
"organization": "EESTEC",
"startDate": "2012-05-01",
"position": "Oversight Committee",
"highlights": [
"Supervised the work of the International Board"
]
}
],
"education": [
{
"endDate": "2012-10-01",
"studyType": "Bachelor+Master",
"area": "Telecommunications Engineering",
"gpa": "",
"courses": [
"Computer Networks",
"Software Engineering",
"Web Technologies"
],
"institution": "Tecnical University of Madrid (UPM)",
"startDate": "2007-10-01"
}
],
"languages": [
{
"fluency": "Native speaker",
"language": "Spanish"
},
{
"fluency": "Professional competency",
"language": "English"
}
],
"references": [],
"awards": [],
"basics": {
"picture": "http://jfernando.es/me.jpg",
"summary": "",
"email": "jfsanchezrada@gmail.com",
"location": {
"countryCode": "ES",
"region": "Madrid",
"postalCode": "",
"address": "",
"city": "Madrid"
},
"label": "Researcher",
"website": "http://jfernando.es",
"profiles": [
{
"network": "Twitter",
"username": "balkian",
"url": "http://twitter.com/balkian"
},
{
"network": "linkedin",
"username": "balkian",
"url": "linkedin.com/in/jfsanchezrada"
},
{
"network": "Github",
"username": "balkian",
"url": "http://github.com/balkian"
}
],
"phone": "",
"name": "J. Fernando S\u00e1nchez"
},
"work": [
{
"endDate": "",
"website": "http://www.gsi.dit.upm.es",
"summary": "The Ingelligent Systems Group is a research group at Universidad Polit\u00e9cnica de Madrid (UPM)",
"company": "GSI UPM",
"startDate": "2013-01-01",
"position": "Researcher - PhD Student",
"highlights": [
"Semantic Technologies",
"Sentiment Analysis",
"Ontologies and vocabularies: Marl, Onyx"
]
},
{
"endDate": "2012-12-01",
"website": "http://www.gsi.dit.upm.es",
"summary": "",
"company": "GSI UPM",
"startDate": "2008-06-01",
"position": "Undergraduate researcher",
"highlights": [
"Web services: Node.js, Python, PHP, JSP/J2EE...",
"Semantic technologies: rdflib, easy-rdf, Proteg\u00e9",
"Event middleware and messaging: XMPP, developed Maia"
]
}
],
"skills": [
{
"keywords": [
"HTML",
"CSS",
"Javascript"
],
"level": "Master",
"name": "Web Development"
}
]
}
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