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https://github.com/gsi-upm/senpy
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Merge branch 'master' into 44-add-basic-evaluation-with-gsitk
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5e2ada1654
@ -1,8 +1,11 @@
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What is Senpy?
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--------------
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Web services can get really complex: data validation, user interaction, formatting, logging., etc.
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The figure below summarizes the typical features in an analysis service.
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Senpy is a framework for text analysis using Linked Data. There are three main applications of Senpy so far: sentiment and emotion analysis, user profiling and entity recoginition. Annotations and Services are compliant with NIF (NLP Interchange Format).
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Senpy aims at providing a framework where analysis modules can be integrated easily as plugins, and providing a core functionality for managing tasks such as data validation, user interaction, formatting, logging, translation to linked data, etc.
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The figure below summarizes the typical features in a text analysis service.
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Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
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.. image:: senpy-framework.png
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@ -1,8 +1,24 @@
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Vocabularies and model
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======================
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The model used in Senpy is based on the following vocabularies:
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The model used in Senpy is based on NIF 2.0 [1], which defines a semantic format and API for improving interoperability among natural language processing services.
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* Marl, a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
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* Onyx, which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
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* NIF 2.0, which defines a semantic format and APO for improving interoperability among natural language processing services
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Senpy has been applied to sentiment and emotion analysis services using the following vocabularies:
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* Marl [2,6], a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
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* Onyx [3,5], which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
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An overview of the vocabularies and their use can be found in [4].
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[1] Guidelines for developing NIF-based NLP services, Final Community Group Report 22 December 2015 Available at: https://www.w3.org/2015/09/bpmlod-reports/nif-based-nlp-webservices/
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[2] Marl Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/marl/
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[3] Onyx Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/onyx/
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[4] Iglesias, C. A., Sánchez-Rada, J. F., Vulcu, G., & Buitelaar, P. (2017). Linked Data Models for Sentiment and Emotion Analysis in Social Networks. In Sentiment Analysis in Social Networks (pp. 49-69).
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[5] Sánchez-Rada, J. F., & Iglesias, C. A. (2016). Onyx: A linked data approach to emotion representation. Information Processing & Management, 52(1), 99-114.
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[6] Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011). Linked opinions: Describing sentiments on the structured web of data.
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@ -22,6 +22,7 @@ the server.
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from flask import Flask
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from senpy.extensions import Senpy
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from senpy.utils import easy_test
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import logging
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import os
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@ -39,7 +40,7 @@ def main():
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'-l',
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metavar='logging_level',
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type=str,
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default="ERROR",
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default="WARN",
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help='Logging level')
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parser.add_argument(
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'--debug',
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@ -75,6 +76,12 @@ def main():
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action='store_true',
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default=False,
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help='Do not run a server, only install plugin dependencies')
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parser.add_argument(
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'--only-test',
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'-t',
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action='store_true',
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default=False,
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help='Do not run a server, just test all plugins')
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parser.add_argument(
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'--only-list',
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'--list',
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@ -122,6 +129,9 @@ def main():
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if args.only_install:
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return
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sp.activate_all()
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if args.only_test:
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easy_test(sp.plugins())
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return
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print('Senpy version {}'.format(senpy.__version__))
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print('Server running on port %s:%d. Ctrl+C to quit' % (args.host,
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args.port))
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@ -16,7 +16,6 @@ import os
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import copy
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import errno
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import logging
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import traceback
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#Correct this import for managing the datasets
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from gsitk.datasets.datasets import DatasetManager
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@ -176,22 +175,14 @@ class Senpy(object):
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by api.parse_call().
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"""
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logger.debug("analysing request: {}".format(request))
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try:
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entries = request.entries
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request.entries = []
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plugins = self._get_plugins(request)
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results = request
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for i in self._process_entries(entries, results, plugins):
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results.entries.append(i)
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self.convert_emotions(results)
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logger.debug("Returning analysis result: {}".format(results))
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except (Error, Exception) as ex:
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if not isinstance(ex, Error):
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msg = "Error during analysis: {} \n\t{}".format(ex,
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traceback.format_exc())
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ex = Error(message=msg, status=500)
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logger.exception('Error returning analysis result')
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raise ex
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entries = request.entries
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request.entries = []
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plugins = self._get_plugins(request)
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results = request
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for i in self._process_entries(entries, results, plugins):
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results.entries.append(i)
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self.convert_emotions(results)
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logger.debug("Returning analysis result: {}".format(results))
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results.analysis = [i['plugin'].id for i in results.analysis]
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return results
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@ -327,6 +327,7 @@ for i in [
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'emotionModel',
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'emotionPlugin',
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'emotionSet',
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'entity',
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'help',
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'plugin',
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'plugins',
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@ -42,7 +42,7 @@ class Sentiment140Plugin(SentimentPlugin):
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from requests.
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'''
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from senpy.test import patch_requests
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expected = {"data": [{"polarity": 10}]}
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expected = {"data": [{"polarity": 4}]}
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with patch_requests(expected) as (request, response):
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super(Sentiment140Plugin, self).test(*args, **kwargs)
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assert request.called
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@ -3,6 +3,8 @@ try:
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except ImportError:
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from mock import patch, MagicMock
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from past.builtins import basestring
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import json
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from contextlib import contextmanager
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@ -15,13 +17,17 @@ def patch_requests(value, code=200):
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success = MagicMock()
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if isinstance(value, BaseModel):
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value = value.jsonld()
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data = json.dumps(value)
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if not isinstance(value, basestring):
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data = json.dumps(value)
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else:
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data = value
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success.json.return_value = value
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success.data.return_value = data
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success.status_code = code
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success.content = json.dumps(value)
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success.status_code = code
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success.content = data
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success.text = data
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method_mocker = MagicMock()
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method_mocker.return_value = success
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with patch.multiple('requests', request=method_mocker,
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logger.setLevel(logging.DEBUG)
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logging.getLogger().setLevel(logging.INFO)
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if not plugin_list:
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from . import plugins
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import __main__
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logger.info('Loading classes from {}'.format(__main__))
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from . import plugins
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plugin_list = plugins.from_module(__main__)
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for plug in plugin_list:
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plug.test()
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@ -182,8 +182,7 @@ class ExtensionsTest(TestCase):
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analyse(self.senpy, input='nothing', algorithm='MOCK')
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assert False
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except Exception as ex:
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assert 'generic exception on analysis' in ex['message']
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assert ex['status'] == 500
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assert 'generic exception on analysis' in str(ex)
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def test_filtering(self):
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""" Filtering plugins """
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EmotionAnalysis,
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EmotionSet,
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Entry,
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Entity,
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Error,
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Results,
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Sentiment,
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@ -207,3 +208,14 @@ class ModelsTest(TestCase):
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recovered = from_string(string)
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assert isinstance(recovered, Results)
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assert isinstance(recovered.entries[0], Entry)
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def test_serializable(self):
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r = Results()
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e = Entry()
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ent = Entity()
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e.entities.append(ent)
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r.entries.append(e)
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d = r.serializable()
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assert d
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assert d['entries']
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assert d['entries'][0]['entities']
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32
tests/test_test.py
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32
tests/test_test.py
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from unittest import TestCase
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import requests
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import json
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from senpy.test import patch_requests
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from senpy.models import Results
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class TestTest(TestCase):
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def test_patch_text(self):
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with patch_requests('hello'):
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r = requests.get('http://example.com')
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assert r.text == 'hello'
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assert r.content == 'hello'
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def test_patch_json(self):
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r = Results()
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with patch_requests(r):
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res = requests.get('http://example.com')
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assert res.content == json.dumps(r.jsonld())
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js = res.json()
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assert js
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assert js['@type'] == r['@type']
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def test_patch_dict(self):
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r = {'nothing': 'new'}
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with patch_requests(r):
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res = requests.get('http://example.com')
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assert res.content == json.dumps(r)
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js = res.json()
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assert js
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assert js['nothing'] == 'new'
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