mirror of
https://github.com/gsi-upm/senpy
synced 2024-11-14 04:32:29 +00:00
Merged into monorepo
This commit is contained in:
J. Fernando Sánchez
parent
e51b659030
commit
c52a894017
24
.gitmodules
vendored
24
.gitmodules
vendored
@ -1,15 +1,9 @@
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[submodule "sentiment-meaningCloud"]
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path = sentiment-meaningCloud
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url = https://lab.cluster.gsi.dit.upm.es/senpy/sentiment-meaningCloud/
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[submodule "sentiment-vader"]
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path = sentiment-vader
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url = https://lab.cluster.gsi.dit.upm.es/senpy/sentiment-vader/
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[submodule "emotion-wnaffect"]
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path = emotion-wnaffect
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url = https://lab.cluster.gsi.dit.upm.es/senpy/emotion-wnaffect
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[submodule "emotion-anew"]
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path = emotion-anew
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url = https://lab.cluster.gsi.dit.upm.es/senpy/emotion-anew
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[submodule "sentiment-basic"]
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path = sentiment-basic
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url = https://lab.cluster.gsi.dit.upm.es/senpy/sentiment-basic
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[submodule "emotion-anew/data"]
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path = emotion-anew/data
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url = https://lab.cluster.gsi.dit.upm.es/senpy/data/emotion-anew.git
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[submodule "emotion-wnaffect/data"]
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path = emotion-wnaffect/data
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url = https://lab.cluster.gsi.dit.upm.es/senpy/data/emotion-wnaffect.git
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[submodule "sentiment-basic/data"]
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path = sentiment-basic/data
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url = https://lab.cluster.gsi.dit.upm.es/senpy/data/sentiment-basic.git
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@ -1,9 +1,10 @@
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from gsiupm/senpy:0.8.7-python2.7
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from gsiupm/senpy:0.10.5-python2.7
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RUN python -m nltk.downloader stopwords
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RUN python -m nltk.downloader punkt
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RUN python -m nltk.downloader maxent_treebank_pos_tagger
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RUN python -m nltk.downloader wordnet
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RUN python -m nltk.downloader omw
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ADD . /senpy-plugins
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11
Makefile
11
Makefile
@ -3,6 +3,11 @@ NAME=senpycommunity
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REPO=gsiupm
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VERSION=test
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PLUGINS= $(filter %/, $(wildcard */))
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DOCKER_FLAGS=
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ifdef SENPY_FOLDER
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DOCKER_FLAGS+=-v $(realpath $(SENPY_FOLDER)):/usr/src/app/
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endif
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all: build run
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@ -11,15 +16,15 @@ build: clean Dockerfile
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docker build -t '$(REPO)/$(NAME):$(VERSION)-python$(PYVERSION)' -f Dockerfile .;
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test-%:
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docker run -v $$PWD/$*:/senpy-plugins/ --rm --entrypoint=/usr/local/bin/py.test -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYVERSION)' test.py
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docker run $(DOCKER_FLAGS) -v $$PWD/$*:/senpy-plugins/ --rm -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYVERSION)' --only-test $(TEST_FLAGS)
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test: $(addprefix test-,$(PLUGINS))
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test: test-.
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clean:
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@docker ps -a | awk '/$(REPO)\/$(NAME)/{ split($$2, vers, "-"); if(vers[1] != "${VERSION}"){ print $$1;}}' | xargs docker rm 2>/dev/null|| true
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@docker images | awk '/$(REPO)\/$(NAME)/{ split($$2, vers, "-"); if(vers[1] != "${VERSION}"){ print $$1":"$$2;}}' | xargs docker rmi 2>/dev/null|| true
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run: build
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docker run --rm -p 5000:5000 -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYMAIN)'
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docker run $(DOCKER_FLAGS) --rm -p 5000:5000 -ti '$(REPO)/$(NAME):$(VERSION)-python$(PYMAIN)'
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.PHONY: test test-% build-% build test test_pip run clean
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3
emotion-anew/.gitmodules
vendored
3
emotion-anew/.gitmodules
vendored
@ -1,3 +0,0 @@
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[submodule "data"]
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path = data
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url = ../data/emotion-anew
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@ -23,12 +23,81 @@ from senpy.plugins import SentimentPlugin, SenpyPlugin
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from senpy.models import Results, EmotionSet, Entry, Emotion
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class EmotionTextPlugin(SentimentPlugin):
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class ANEW(SentimentPlugin):
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description = "This plugin consists on an emotion classifier using ANEW lexicon dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. Each emotion has a centroid, calculated according to this article: http://www.aclweb.org/anthology/W10-0208. The plugin is going to look for the words in the sentence that appear in the ANEW dictionary and calculate the average VAD score for the sentence. Once this score is calculated, it is going to seek the emotion that is closest to this value."
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author = "@icorcuera"
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version = "0.5.1"
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name = "emotion-anew"
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extra_params = {
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"language": {
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"aliases": ["language", "l"],
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"required": True,
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"options": ["es","en"],
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"default": "en"
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}
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}
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anew_path_es = "Dictionary/Redondo(2007).csv"
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anew_path_en = "Dictionary/ANEW2010All.txt"
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centroids = {
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"anger": {
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"A": 6.95,
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"D": 5.1,
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"V": 2.7
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},
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"disgust": {
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"A": 5.3,
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"D": 8.05,
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"V": 2.7
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},
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"fear": {
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"A": 6.5,
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"D": 3.6,
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"V": 3.2
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},
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"joy": {
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"A": 7.22,
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"D": 6.28,
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"V": 8.6
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},
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"sadness": {
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"A": 5.21,
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"D": 2.82,
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"V": 2.21
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}
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}
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emotions_ontology = {
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"anger": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#anger",
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"disgust": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#disgust",
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"fear": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#negative-fear",
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"joy": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#joy",
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"neutral": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#neutral-emotion",
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"sadness": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#sadness"
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}
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onyx__usesEmotionModel = "emoml:big6"
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nltk_resources = ['stopwords']
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def activate(self, *args, **kwargs):
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nltk.download('stopwords')
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self._stopwords = stopwords.words('english')
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self._local_path=os.path.dirname(os.path.abspath(__file__))
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dictionary={}
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dictionary['es'] = {}
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with self.open(self.anew_path_es,'rb') as tabfile:
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reader = csv.reader(tabfile, delimiter='\t')
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for row in reader:
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dictionary['es'][row[2]]={}
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dictionary['es'][row[2]]['V']=row[3]
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dictionary['es'][row[2]]['A']=row[5]
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dictionary['es'][row[2]]['D']=row[7]
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dictionary['en'] = {}
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with self.open(self.anew_path_en,'rb') as tabfile:
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reader = csv.reader(tabfile, delimiter='\t')
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for row in reader:
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dictionary['en'][row[0]]={}
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dictionary['en'][row[0]]['V']=row[2]
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dictionary['en'][row[0]]['A']=row[4]
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dictionary['en'][row[0]]['D']=row[6]
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self._dictionary = dictionary
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def _my_preprocessor(self, text):
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@ -116,41 +185,76 @@ class EmotionTextPlugin(SentimentPlugin):
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def analyse_entry(self, entry, params):
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text_input = entry.get("text", None)
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text_input = entry.text
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text= self._my_preprocessor(text_input)
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dictionary={}
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lang = params.get("language", "auto")
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if lang == 'es':
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with open(self._local_path + self.anew_path_es,'rb') as tabfile:
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reader = csv.reader(tabfile, delimiter='\t')
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for row in reader:
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dictionary[row[2]]={}
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dictionary[row[2]]['V']=row[3]
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dictionary[row[2]]['A']=row[5]
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dictionary[row[2]]['D']=row[7]
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else:
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with open(self._local_path + self.anew_path_en,'rb') as tabfile:
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reader = csv.reader(tabfile, delimiter='\t')
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for row in reader:
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dictionary[row[0]]={}
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dictionary[row[0]]['V']=row[2]
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dictionary[row[0]]['A']=row[4]
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dictionary[row[0]]['D']=row[6]
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text = self._my_preprocessor(text_input)
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dictionary = self._dictionary[params['language']]
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feature_set=self._extract_features(text,dictionary,lang)
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feature_set=self._extract_features(text, dictionary, params['language'])
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emotions = EmotionSet()
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emotions.id = "Emotions0"
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emotion1 = Emotion(id="Emotion0")
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emotion1["onyx:hasEmotionCategory"] = self.emotions_ontology[feature_set['emotion']]
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emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#valence"] = feature_set['V']
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emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#arousal"] = feature_set['A']
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emotion1["http://www.gsi.dit.upm.es/ontologies/onyx/vocabularies/anew/ns#dominance"] = feature_set['D']
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emotion1.prov(self)
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emotions.prov(self)
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emotions.onyx__hasEmotion.append(emotion1)
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entry.emotions = [emotions,]
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entry.emotions = [emotions, ]
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yield entry
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ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
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test_cases = [
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{
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'input': 'I hate you',
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'expected': {
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'emotions': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'anger',
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}]
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}]
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}
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}, {
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'input': 'i am sad',
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'expected': {
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'emotions': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'sadness',
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}]
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}]
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}
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}, {
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'input': 'i am happy with my marks',
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'expected': {
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'emotions': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'joy',
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}]
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}]
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}
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}, {
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'input': 'This movie is scary',
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'expected': {
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'emotions': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'negative-fear',
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}]
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}]
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}
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}, {
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'input': 'this cake is disgusting' ,
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'expected': {
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'emotions': [{
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'onyx:hasEmotion': [{
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'onyx:hasEmotionCategory': ontology + 'negative-fear',
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}]
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}]
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}
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}
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]
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Binary file not shown.
@ -1,64 +1,12 @@
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{
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"name": "emotion-anew",
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"module": "emotion-anew",
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"description": "This plugin consists on an emotion classifier using ANEW lexicon dictionary to calculate VAD (valence-arousal-dominance) of the sentence and determinate which emotion is closer to this value. Each emotion has a centroid, calculated according to this article: http://www.aclweb.org/anthology/W10-0208. The plugin is going to look for the words in the sentence that appear in the ANEW dictionary and calculate the average VAD score for the sentence. Once this score is calculated, it is going to seek the emotion that is closest to this value.",
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"author": "@icorcuera",
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"version": "0.5",
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"extra_params": {
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"language": {
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"aliases": ["language", "l"],
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"required": true,
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"options": ["es","en"],
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"default": "en"
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}
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},
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"requirements": {},
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"anew_path_es": "/data/Dictionary/Redondo(2007).csv",
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"anew_path_en": "/data/Dictionary/ANEW2010All.txt",
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"centroids": {
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"anger": {
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"A": 6.95,
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"D": 5.1,
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"V": 2.7
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},
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"disgust": {
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"A": 5.3,
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"D": 8.05,
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"V": 2.7
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},
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"fear": {
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"A": 6.5,
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"D": 3.6,
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"V": 3.2
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},
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"joy": {
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"A": 7.22,
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"D": 6.28,
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"V": 8.6
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},
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"sadness": {
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"A": 5.21,
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"D": 2.82,
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"V": 2.21
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}
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},
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"emotions_ontology": {
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"anger": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#anger",
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"disgust": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#disgust",
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"fear": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#negative-fear",
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"joy": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#joy",
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"neutral": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#neutral-emotion",
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"sadness": "http://gsi.dit.upm.es/ontologies/wnaffect/ns#sadness"
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},
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"requirements": [
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"numpy",
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"pandas",
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"nltk",
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"scipy",
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"scikit-learn",
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"textblob",
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"pattern",
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"lxml"
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],
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"onyx:usesEmotionModel": "emoml:big6",
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}
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---
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module: emotion-anew
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requirements:
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- numpy
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- pandas
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- nltk
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- scipy
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- scikit-learn
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- textblob
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- pattern
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- lxml
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onyx:usesEmotionModel: "emoml:big6"
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@ -1,45 +0,0 @@
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import os
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import logging
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logging.basicConfig()
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try:
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import unittest.mock as mock
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except ImportError:
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import mock
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from senpy.extensions import Senpy
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from flask import Flask
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import unittest
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import re
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class emoTextANEWTest(unittest.TestCase):
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def setUp(self):
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self.app = Flask("test_plugin")
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self.dir = os.path.join(os.path.dirname(__file__))
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self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
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self.senpy.init_app(self.app)
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def tearDown(self):
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self.senpy.deactivate_plugin("EmoTextANEW", sync=True)
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def test_analyse(self):
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plugin = self.senpy.plugins["EmoTextANEW"]
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plugin.activate()
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ontology = "http://gsi.dit.upm.es/ontologies/wnaffect/ns#"
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texts = {'I hate you': 'anger',
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'i am sad': 'sadness',
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'i am happy with my marks': 'joy',
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'This movie is scary': 'negative-fear',
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'this cake is disgusting' : 'negative-fear'}
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|
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for text in texts:
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response = plugin.analyse(input=text)
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expected = texts[text]
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||||
emotionSet = response.entries[0].emotions[0]
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||||
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||||
assert emotionSet['onyx:hasEmotion'][0]['onyx:hasEmotionCategory'] == ontology+expected
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|
||||
plugin.deactivate()
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||||
|
||||
if __name__ == '__main__':
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||||
unittest.main()
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3
emotion-wnaffect/.gitmodules
vendored
3
emotion-wnaffect/.gitmodules
vendored
@ -1,3 +0,0 @@
|
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[submodule "data"]
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path = data
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url = ../data/emotion-wnaffect
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@ -2,7 +2,6 @@
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from __future__ import division
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import re
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||||
import nltk
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import logging
|
||||
import os
|
||||
import string
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||||
import xml.etree.ElementTree as ET
|
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@ -14,11 +13,29 @@ from senpy.plugins import EmotionPlugin, AnalysisPlugin, ShelfMixin
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from senpy.models import Results, EmotionSet, Entry, Emotion
|
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|
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|
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class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
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'''Emotion classifier using WordNet-Affect to calculate the percentage
|
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class WNAffect(EmotionPlugin, ShelfMixin):
|
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'''
|
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Emotion classifier using WordNet-Affect to calculate the percentage
|
||||
of each emotion. This plugin classifies among 6 emotions: anger,fear,disgust,joy,sadness
|
||||
or neutral. The only available language is English (en)
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||||
'''
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||||
name = 'emotion-wnaffect'
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||||
author = ["@icorcuera", "@balkian"]
|
||||
version = '0.2'
|
||||
extra_params = {
|
||||
'language': {
|
||||
"@id": 'lang_wnaffect',
|
||||
'aliases': ['language', 'l'],
|
||||
'required': True,
|
||||
'options': ['en',]
|
||||
}
|
||||
}
|
||||
synsets_path = "a-synsets.xml"
|
||||
hierarchy_path = "a-hierarchy.xml"
|
||||
wn16_path = "wordnet1.6/dict"
|
||||
onyx__usesEmotionModel = "emoml:big6"
|
||||
nltk_resources = ['stopwords', 'averaged_perceptron_tagger', 'wordnet']
|
||||
|
||||
def _load_synsets(self, synsets_path):
|
||||
"""Returns a dictionary POS tag -> synset offset -> emotion (str -> int -> str)."""
|
||||
tree = ET.parse(synsets_path)
|
||||
@ -56,7 +73,6 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
|
||||
|
||||
def activate(self, *args, **kwargs):
|
||||
|
||||
nltk.download(['stopwords', 'averaged_perceptron_tagger', 'wordnet'])
|
||||
self._stopwords = stopwords.words('english')
|
||||
self._wnlemma = wordnet.WordNetLemmatizer()
|
||||
self._syntactics = {'N': 'n', 'V': 'v', 'J': 'a', 'S': 's', 'R': 'r'}
|
||||
@ -87,16 +103,16 @@ class EmotionTextPlugin(EmotionPlugin, ShelfMixin):
|
||||
'sadness': 'sadness'
|
||||
}
|
||||
|
||||
self._load_emotions(local_path + self.hierarchy_path)
|
||||
self._load_emotions(self.find_file(self.hierarchy_path))
|
||||
|
||||
if 'total_synsets' not in self.sh:
|
||||
total_synsets = self._load_synsets(local_path + self.synsets_path)
|
||||
total_synsets = self._load_synsets(self.find_file(self.synsets_path))
|
||||
self.sh['total_synsets'] = total_synsets
|
||||
|
||||
self._total_synsets = self.sh['total_synsets']
|
||||
|
||||
self._wn16_path = self.wn16_path
|
||||
self._wn16 = WordNetCorpusReader(os.path.abspath("{0}".format(local_path + self._wn16_path)), nltk.data.find(local_path + self._wn16_path))
|
||||
self._wn16 = WordNetCorpusReader(self.find_file(self._wn16_path), nltk.data.find(self.find_file(self._wn16_path)))
|
||||
|
||||
|
||||
def deactivate(self, *args, **kwargs):
|
||||
|
||||
@ -1,24 +1,5 @@
|
||||
---
|
||||
name: emotion-wnaffect
|
||||
module: emotion-wnaffect
|
||||
description: 'Emotion classifier using WordNet-Affect to calculate the percentage
|
||||
of each emotion. This plugin classifies among 6 emotions: anger,fear,disgust,joy,sadness
|
||||
or neutral. The only available language is English (en)'
|
||||
author: "@icorcuera @balkian"
|
||||
version: '0.2'
|
||||
extra_params:
|
||||
language:
|
||||
"@id": lang_wnaffect
|
||||
aliases:
|
||||
- language
|
||||
- l
|
||||
required: false
|
||||
options:
|
||||
- en
|
||||
synsets_path: "/a-synsets.xml"
|
||||
hierarchy_path: "/a-hierarchy.xml"
|
||||
wn16_path: "/wordnet1.6/dict"
|
||||
onyx:usesEmotionModel: emoml:big6
|
||||
requirements:
|
||||
- nltk>=3.0.5
|
||||
- lxml>=3.4.2
|
||||
|
||||
@ -17,7 +17,9 @@ from nltk.corpus import WordNetCorpusReader
|
||||
import xml.etree.ElementTree as ET
|
||||
|
||||
class WNAffect:
|
||||
"""WordNet-Affect ressource."""
|
||||
"""WordNet-Affect resource."""
|
||||
|
||||
nltk_resources = ['averaged_perceptron_tagger']
|
||||
|
||||
def __init__(self, wordnet16_dir, wn_domains_dir):
|
||||
"""Initializes the WordNet-Affect object."""
|
||||
|
||||
@ -1,19 +0,0 @@
|
||||
from senpy.plugins import SentimentPlugin
|
||||
from senpy.models import Response, Entry
|
||||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ExamplePlugin(SentimentPlugin):
|
||||
|
||||
def analyse(self, *args, **kwargs):
|
||||
logger.warn('Analysing with the example.')
|
||||
logger.warn('The answer to this response is: %s.' % kwargs['parameter'])
|
||||
resp = Response()
|
||||
ent = Entry(kwargs['input'])
|
||||
ent['example:reversed'] = kwargs['input'][::-1]
|
||||
ent['example:the_answer'] = kwargs['parameter']
|
||||
resp.entries.append(ent)
|
||||
|
||||
return resp
|
||||
@ -1,17 +0,0 @@
|
||||
{
|
||||
"name": "ExamplePlugin",
|
||||
"module": "example",
|
||||
"description": "I am just an example",
|
||||
"author": "@balkian",
|
||||
"version": "0.1",
|
||||
"extra_params": {
|
||||
"parameter": {
|
||||
"@id": "parameter",
|
||||
"aliases": ["parameter", "param"],
|
||||
"required": true,
|
||||
"default": 42
|
||||
}
|
||||
},
|
||||
"requirements": ["noop"],
|
||||
"custom_attribute": "42"
|
||||
}
|
||||
37
example-plugin/example_plugin.py
Normal file
37
example-plugin/example_plugin.py
Normal file
@ -0,0 +1,37 @@
|
||||
from senpy.plugins import Analysis
|
||||
from senpy.models import Response, Entry
|
||||
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class ExamplePlugin(Analysis):
|
||||
'''A *VERY* simple plugin that exemplifies the development of Senpy Plugins'''
|
||||
name = "example-plugin"
|
||||
author = "@balkian"
|
||||
version = "0.1"
|
||||
extra_params = {
|
||||
"parameter": {
|
||||
"@id": "parameter",
|
||||
"aliases": ["parameter", "param"],
|
||||
"required": True,
|
||||
"default": 42
|
||||
}
|
||||
}
|
||||
custom_attribute = "42"
|
||||
|
||||
def analyse_entry(self, entry, params):
|
||||
logger.debug('Analysing with the example.')
|
||||
logger.debug('The answer to this response is: %s.' % params['parameter'])
|
||||
resp = Response()
|
||||
entry['example:reversed'] = entry.text[::-1]
|
||||
entry['example:the_answer'] = params['parameter']
|
||||
|
||||
yield entry
|
||||
|
||||
test_cases = [{
|
||||
'input': 'hello',
|
||||
'expected': {
|
||||
'example:reversed': 'olleh'
|
||||
}
|
||||
}]
|
||||
@ -1,23 +0,0 @@
|
||||
import unittest
|
||||
from flask import Flask
|
||||
import os
|
||||
|
||||
from senpy.extensions import Senpy
|
||||
|
||||
class emoTextWAFTest(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.app = Flask("Example")
|
||||
self.dir = os.path.join(os.path.dirname(__file__))
|
||||
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
|
||||
self.senpy.init_app(self.app)
|
||||
|
||||
def tearDown(self):
|
||||
self.senpy.deactivate_plugin("ExamplePlugin", sync=True)
|
||||
|
||||
def test_analyse(self):
|
||||
assert len(self.senpy.plugins.keys()) == 1
|
||||
assert True
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
3
sentiment-basic/.gitmodules
vendored
3
sentiment-basic/.gitmodules
vendored
@ -1,3 +0,0 @@
|
||||
[submodule "data"]
|
||||
path = data
|
||||
url = ../data/sentiment-basic
|
||||
@ -1,5 +1,6 @@
|
||||
#!/usr/bin/python
|
||||
# -*- coding: utf-8 -*-
|
||||
import os
|
||||
import logging
|
||||
import string
|
||||
import nltk
|
||||
import pickle
|
||||
@ -13,24 +14,40 @@ from os import path
|
||||
from senpy.plugins import SentimentPlugin, SenpyPlugin
|
||||
from senpy.models import Results, Entry, Sentiment
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class SentiTextPlugin(SentimentPlugin):
|
||||
class SentimentBasic(SentimentPlugin):
|
||||
'''
|
||||
Sentiment classifier using rule-based classification for Spanish. Based on english to spanish translation and SentiWordNet sentiment knowledge. This is a demo plugin that uses only some features from the TASS 2015 classifier. To use the entirely functional classifier you can use the service in: http://senpy.cluster.gsi.dit.upm.es.
|
||||
'''
|
||||
name = "sentiment-basic"
|
||||
author = "github.com/nachtkatze"
|
||||
version = "0.1.1"
|
||||
extra_params = {
|
||||
"language": {
|
||||
"aliases": ["language", "l"],
|
||||
"required": True,
|
||||
"options": ["en","es", "it", "fr", "auto"],
|
||||
"default": "auto"
|
||||
}
|
||||
}
|
||||
sentiword_path = "SentiWordNet_3.0.txt"
|
||||
pos_path = "unigram_spanish.pickle"
|
||||
maxPolarityValue = 1
|
||||
minPolarityValue = -1
|
||||
nltk_resources = ['punkt','wordnet']
|
||||
|
||||
def _load_swn(self):
|
||||
self.swn_path = path.join(path.abspath(path.dirname(__file__)), self.sentiword_path)
|
||||
self.swn_path = self.find_file(self.sentiword_path)
|
||||
swn = SentiWordNet(self.swn_path)
|
||||
return swn
|
||||
|
||||
def _load_pos_tagger(self):
|
||||
self.pos_path = path.join(path.abspath(path.dirname(__file__)), self.pos_path)
|
||||
self.pos_path = self.find_file(self.pos_path)
|
||||
with open(self.pos_path, 'r') as f:
|
||||
tagger = pickle.load(f)
|
||||
return tagger
|
||||
|
||||
def activate(self, *args, **kwargs):
|
||||
nltk.download(['punkt','wordnet'])
|
||||
self._swn = self._load_swn()
|
||||
self._pos_tagger = self._load_pos_tagger()
|
||||
|
||||
@ -54,11 +71,6 @@ class SentiTextPlugin(SentimentPlugin):
|
||||
tokens[i]['tokens'] = self._pos_tagger.tag(tokens[i]['tokens'])
|
||||
return tokens
|
||||
|
||||
# def _stopwords(sentences, lang='english'):
|
||||
# for i in sentences:
|
||||
# sentences[i]['tokens'] = [t for t in sentences[i]['tokens'] if t not in nltk.corpus.stopwords.words(lang)]
|
||||
# return sentences
|
||||
|
||||
def _compare_synsets(self, synsets, tokens, i):
|
||||
for synset in synsets:
|
||||
for word in tokens[i]['lemmas']:
|
||||
@ -71,7 +83,7 @@ class SentiTextPlugin(SentimentPlugin):
|
||||
|
||||
def analyse_entry(self, entry, params):
|
||||
language = params.get("language")
|
||||
text = entry.get("text", None)
|
||||
text = entry.text
|
||||
tokens = self._tokenize(text)
|
||||
tokens = self._pos(tokens)
|
||||
sufixes = {'es':'spa','en':'eng','it':'ita','fr':'fra'}
|
||||
@ -130,19 +142,41 @@ class SentiTextPlugin(SentimentPlugin):
|
||||
except:
|
||||
if n_pos == 0 and n_neg == 0:
|
||||
g_score = 0.5
|
||||
polarity = 'marl:Neutral'
|
||||
polarity_value = 0
|
||||
if g_score > 0.5:
|
||||
if g_score >= 0.5:
|
||||
polarity = 'marl:Positive'
|
||||
polarity_value = 1
|
||||
elif g_score < 0.5:
|
||||
polarity = 'marl:Negative'
|
||||
polarity_value = -1
|
||||
else:
|
||||
polarity = 'marl:Neutral'
|
||||
polarity_value = 0
|
||||
opinion = Sentiment(id="Opinion0"+'_'+str(i),
|
||||
marl__hasPolarity=polarity,
|
||||
marl__polarityValue=polarity_value)
|
||||
|
||||
|
||||
opinion.prov(self)
|
||||
entry.sentiments.append(opinion)
|
||||
|
||||
yield entry
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'input': u'Odio ir al cine',
|
||||
'params': {'language': 'es'},
|
||||
'polarity': 'marl:Negative'
|
||||
|
||||
},
|
||||
{
|
||||
'input': u'El cielo está nublado',
|
||||
'params': {'language': 'es'},
|
||||
'polarity': 'marl:Positive'
|
||||
|
||||
},
|
||||
{
|
||||
'input': u'Esta tarta está muy buena',
|
||||
'params': {'language': 'es'},
|
||||
'polarity': 'marl:Negative'
|
||||
|
||||
}
|
||||
]
|
||||
|
||||
@ -1,24 +1,7 @@
|
||||
{
|
||||
"name": "sentiment-basic",
|
||||
"module": "sentiment-basic",
|
||||
"description": "Sentiment classifier using rule-based classification for Spanish. Based on english to spanish translation and SentiWordNet sentiment knowledge. This is a demo plugin that uses only some features from the TASS 2015 classifier. To use the entirely functional classifier you can use the service in: http://senpy.cluster.gsi.dit.upm.es.",
|
||||
"author": "github.com/nachtkatze",
|
||||
"version": "0.1",
|
||||
"requirements": [
|
||||
"nltk>=3.0.5",
|
||||
"scipy>=0.14.0",
|
||||
"textblob"
|
||||
],
|
||||
"extra_params": {
|
||||
"language": {
|
||||
"aliases": ["language", "l"],
|
||||
"required": true,
|
||||
"options": ["en","es", "it", "fr", "auto"],
|
||||
"default": "auto"
|
||||
},
|
||||
},
|
||||
"sentiword_path": "data/SentiWordNet_3.0.txt",
|
||||
"pos_path": "data/unigram_spanish.pickle",
|
||||
"maxPolarityValue": "1",
|
||||
"minPolarityValue": "-1"
|
||||
}
|
||||
---
|
||||
module: sentiment-basic
|
||||
requirements:
|
||||
- nltk>=3.0.5
|
||||
- scipy>=0.14.0
|
||||
- textblob
|
||||
|
||||
|
||||
@ -46,7 +46,7 @@ class SentiWordNet(object):
|
||||
pos,syn_set_id,pos_score,neg_score,syn_set_score,\
|
||||
gloss = fields
|
||||
except:
|
||||
print "Found data without all details"
|
||||
print("Found data without all details")
|
||||
pass
|
||||
|
||||
if pos and syn_set_score:
|
||||
|
||||
@ -1,42 +0,0 @@
|
||||
import os
|
||||
import logging
|
||||
logging.basicConfig()
|
||||
try:
|
||||
import unittest.mock as mock
|
||||
except ImportError:
|
||||
import mock
|
||||
from senpy.extensions import Senpy
|
||||
from flask import Flask
|
||||
import unittest
|
||||
|
||||
class SentiTextTest(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.app = Flask("test_plugin")
|
||||
self.dir = os.path.join(os.path.dirname(__file__))
|
||||
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
|
||||
self.senpy.init_app(self.app)
|
||||
|
||||
def tearDown(self):
|
||||
self.senpy.deactivate_plugin("SentiText", sync=True)
|
||||
|
||||
def test_analyse(self):
|
||||
plugin = self.senpy.plugins["SentiText"]
|
||||
plugin.activate()
|
||||
|
||||
texts = {'Odio ir al cine' : 'marl:Neutral',
|
||||
'El cielo esta nublado' : 'marl:Positive',
|
||||
'Esta tarta esta muy buena' : 'marl:Neutral'}
|
||||
|
||||
for text in texts:
|
||||
response = plugin.analyse(input=text)
|
||||
sentimentSet = response.entries[0].sentiments[0]
|
||||
print sentimentSet
|
||||
expected = texts[text]
|
||||
|
||||
assert sentimentSet['marl:hasPolarity'] == expected
|
||||
|
||||
plugin.deactivate()
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -1,7 +1,6 @@
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import time
|
||||
import logging
|
||||
import requests
|
||||
import json
|
||||
import string
|
||||
@ -9,20 +8,33 @@ import os
|
||||
from os import path
|
||||
import time
|
||||
from senpy.plugins import SentimentPlugin
|
||||
from senpy.models import Results, Entry, Sentiment, Error
|
||||
from senpy.models import Results, Entry, Entity, Topic, Sentiment, Error
|
||||
from senpy.utils import check_template
|
||||
|
||||
import mocked_request
|
||||
from mocked_request import mocked_requests_post
|
||||
|
||||
try:
|
||||
from unittest import mock
|
||||
except ImportError:
|
||||
import mock
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class MeaningCloudPlugin(SentimentPlugin):
|
||||
version = "0.1"
|
||||
'''
|
||||
Sentiment analysis with meaningCloud service.
|
||||
To use this plugin, you need to obtain an API key from meaningCloud signing up here:
|
||||
https://www.meaningcloud.com/developer/login
|
||||
|
||||
When you had obtained the meaningCloud API Key, you have to provide it to the plugin, using param apiKey.
|
||||
Example request:
|
||||
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=meaningCloud&language=en&apiKey=<API key>&input=I%20love%20Madrid.
|
||||
'''
|
||||
name = 'sentiment-meaningcloud'
|
||||
author = 'GSI UPM'
|
||||
version = "1.1"
|
||||
maxPolarityValue = 1
|
||||
minPolarityValue = -1
|
||||
|
||||
extra_params = {
|
||||
"language": {
|
||||
@ -37,7 +49,6 @@ class MeaningCloudPlugin(SentimentPlugin):
|
||||
}
|
||||
}
|
||||
|
||||
"""MeaningCloud plugin uses API from Meaning Cloud to perform sentiment analysis."""
|
||||
def _polarity(self, value):
|
||||
|
||||
if 'NONE' in value:
|
||||
@ -81,7 +92,7 @@ class MeaningCloudPlugin(SentimentPlugin):
|
||||
if not api_response.get('score_tag'):
|
||||
raise Error(r.json())
|
||||
entry['language_detected'] = lang
|
||||
logger.info(api_response)
|
||||
self.log.debug(api_response)
|
||||
agg_polarity, agg_polarityValue = self._polarity(
|
||||
api_response.get('score_tag', None))
|
||||
agg_opinion = Sentiment(
|
||||
@ -89,13 +100,14 @@ class MeaningCloudPlugin(SentimentPlugin):
|
||||
marl__hasPolarity=agg_polarity,
|
||||
marl__polarityValue=agg_polarityValue,
|
||||
marl__opinionCount=len(api_response['sentence_list']))
|
||||
agg_opinion.prov(self)
|
||||
entry.sentiments.append(agg_opinion)
|
||||
logger.info(api_response['sentence_list'])
|
||||
self.log.debug(api_response['sentence_list'])
|
||||
count = 1
|
||||
|
||||
for sentence in api_response['sentence_list']:
|
||||
for nopinion in sentence['segment_list']:
|
||||
logger.info(nopinion)
|
||||
self.log.debug(nopinion)
|
||||
polarity, polarityValue = self._polarity(
|
||||
nopinion.get('score_tag', None))
|
||||
opinion = Sentiment(
|
||||
@ -107,39 +119,37 @@ class MeaningCloudPlugin(SentimentPlugin):
|
||||
nif__beginIndex=nopinion.get('inip', None),
|
||||
nif__endIndex=nopinion.get('endp', None))
|
||||
count += 1
|
||||
opinion.prov(self)
|
||||
entry.sentiments.append(opinion)
|
||||
|
||||
mapper = {'es': 'es.', 'en': '', 'ca': 'es.', 'it':'it.', 'fr':'fr.', 'pt':'pt.'}
|
||||
|
||||
for sent_entity in api_response_topics['entity_list']:
|
||||
|
||||
resource = "_".join(sent_entity.get('form', None).split())
|
||||
entity = Sentiment(
|
||||
entity = Entity(
|
||||
id="Entity{}".format(sent_entity.get('id')),
|
||||
marl__describesObject="http://{}dbpedia.org/resource/{}".format(
|
||||
itsrdf__taIdentRef="http://{}dbpedia.org/resource/{}".format(
|
||||
mapper[lang], resource),
|
||||
nif__anchorOf=sent_entity.get('form', None),
|
||||
nif__beginIndex=sent_entity['variant_list'][0].get('inip', None),
|
||||
nif__endIndex=sent_entity['variant_list'][0].get('endp', None))
|
||||
entity[
|
||||
'@type'] = "ODENTITY_{}".format(
|
||||
sent_entity['sementity'].get('type', None).split(">")[-1])
|
||||
sementity = sent_entity['sementity'].get('type', None).split(">")[-1]
|
||||
entity['@type'] = "ODENTITY_{}".format(sementity)
|
||||
entity.prov(self)
|
||||
entry.entities.append(entity)
|
||||
|
||||
for topic in api_response_topics['concept_list']:
|
||||
if 'semtheme_list' in topic:
|
||||
for theme in topic['semtheme_list']:
|
||||
concept = Sentiment(
|
||||
id="Topic{}".format(topic.get('id')),
|
||||
prov__wasDerivedFrom="http://dbpedia.org/resource/{}".
|
||||
format(theme['type'].split('>')[-1]))
|
||||
concept[
|
||||
'@type'] = "ODTHEME_{}".format(
|
||||
theme['type'].split(">")[-1])
|
||||
concept = Topic()
|
||||
concept.id = "Topic{}".format(topic.get('id'))
|
||||
concept['@type'] = "ODTHEME_{}".format(theme['type'].split(">")[-1])
|
||||
concept['fam:topic-reference'] = "http://dbpedia.org/resource/{}".format(theme['type'].split('>')[-1])
|
||||
entry.prov(self)
|
||||
entry.topics.append(concept)
|
||||
yield entry
|
||||
|
||||
@mock.patch('requests.post', side_effect=mocked_request.mocked_requests_post)
|
||||
@mock.patch('requests.post', side_effect=mocked_requests_post)
|
||||
def test(self, *args, **kwargs):
|
||||
results = list()
|
||||
params = {'algo': 'sentiment-meaningCloud',
|
||||
@ -156,15 +166,16 @@ class MeaningCloudPlugin(SentimentPlugin):
|
||||
'language': 'en',
|
||||
'apikey': '00000',
|
||||
'algorithm': 'sentiment-meaningCloud'}
|
||||
for i in range(100):
|
||||
res = next(self.analyse_entry(Entry(nif__isString="Hello World Obama"), params))
|
||||
results.append(res.sentiments[0]['marl:hasPolarity'])
|
||||
results.append(res.topics[0]['prov:wasDerivedFrom'])
|
||||
results.append(res.entities[0]['prov:wasDerivedFrom'])
|
||||
|
||||
assert 'marl:Neutral' in results
|
||||
assert 'http://dbpedia.org/resource/Astronomy' in results
|
||||
assert 'http://dbpedia.org/resource/Obama' in results
|
||||
check_template(res,
|
||||
{'sentiments': [
|
||||
{'marl:hasPolarity': 'marl:Neutral'}],
|
||||
'entities': [
|
||||
{'itsrdf:taIdentRef': 'http://dbpedia.org/resource/Obama'}],
|
||||
'topics': [
|
||||
{'fam:topic-reference': 'http://dbpedia.org/resource/Astronomy'}]
|
||||
})
|
||||
|
||||
if __name__ == '__main__':
|
||||
from senpy import easy_test
|
||||
@ -7,7 +7,6 @@ def mocked_requests_post(*args, **kwargs):
|
||||
def json(self):
|
||||
return self.json_data
|
||||
|
||||
print("Mocking request")
|
||||
if args[0] == 'http://api.meaningcloud.com/sentiment-2.1':
|
||||
return MockResponse({
|
||||
'model': 'general_en',
|
||||
|
||||
@ -1,11 +0,0 @@
|
||||
{
|
||||
"name": "sentiment-meaningcloud",
|
||||
"module": "sentiment_meaningcloud",
|
||||
"description": "Sentiment analysis with meaningCloud service. To use this plugin, you need to obtain an API key from meaningCloud signing up here: https://www.meaningcloud.com/developer/login. When you had obtained the meaningCloud API Key, you have to provide it to the plugin, using param apiKey. Example request: http://senpy.cluster.gsi.dit.upm.es/api/?algo=meaningCloud&language=en&apiKey=<put here your API key>&input=I%20love%20Madrid.",
|
||||
"author": "GSI UPM",
|
||||
"version": "1.0",
|
||||
"requirements": {},
|
||||
"maxPolarityValue": "1",
|
||||
"minPolarityValue": "-1"
|
||||
|
||||
}
|
||||
@ -1,10 +1,12 @@
|
||||
# Sentimet-vader plugin
|
||||
|
||||
=========
|
||||
|
||||
Vader is a plugin developed at GSI UPM for sentiment analysis.
|
||||
The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web.
|
||||
|
||||
## Acknowledgements
|
||||
|
||||
This plugin uses the vaderSentiment module underneath, which is described in the paper:
|
||||
|
||||
For developing this plugin, it has been used the module vaderSentiment, which is described in the paper:
|
||||
VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text
|
||||
C.J. Hutto and Eric Gilbert
|
||||
Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
|
||||
@ -15,16 +17,16 @@ For more information about the functionality, check the official repository
|
||||
|
||||
https://github.com/cjhutto/vaderSentiment
|
||||
|
||||
The response of this plugin uses [Marl ontology](https://www.gsi.dit.upm.es/ontologies/marl/) developed at GSI UPM for semantic web.
|
||||
|
||||
## Usage
|
||||
|
||||
Params accepted:
|
||||
Parameters:
|
||||
|
||||
- Language: es (Spanish), en(English).
|
||||
- Input: Text to analyse.
|
||||
|
||||
|
||||
Example request:
|
||||
|
||||
```
|
||||
http://senpy.cluster.gsi.dit.upm.es/api/?algo=sentiment-vader&language=en&input=I%20love%20Madrid
|
||||
```
|
||||
|
||||
@ -1,16 +0,0 @@
|
||||
==========
|
||||
|
||||
This README file describes the plugin vaderSentiment.
|
||||
|
||||
For developing this plugin, it has been used the module vaderSentiment, which is described in the paper:
|
||||
VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text
|
||||
C.J. Hutto and Eric Gilbert
|
||||
Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.
|
||||
|
||||
If you use this plugin in your research, please cite the above paper
|
||||
|
||||
For more information about the functionality, check the official repository
|
||||
|
||||
https://github.com/cjhutto/vaderSentiment
|
||||
|
||||
========
|
||||
@ -1,25 +0,0 @@
|
||||
{
|
||||
"name": "sentiment-vader",
|
||||
"module": "sentiment-vader",
|
||||
"description": "Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.",
|
||||
"author": "@icorcuera",
|
||||
"version": "0.1",
|
||||
"extra_params": {
|
||||
"language": {
|
||||
"@id": "lang_rand",
|
||||
"aliases": ["language", "l"],
|
||||
"required": false,
|
||||
"options": ["es", "en", "auto"]
|
||||
},
|
||||
|
||||
"aggregate": {
|
||||
"aliases": ["aggregate","agg"],
|
||||
"options": ["true", "false"],
|
||||
"required": false,
|
||||
"default": false
|
||||
|
||||
}
|
||||
|
||||
},
|
||||
"requirements": {}
|
||||
}
|
||||
@ -1,44 +0,0 @@
|
||||
import os
|
||||
import logging
|
||||
logging.basicConfig()
|
||||
try:
|
||||
import unittest.mock as mock
|
||||
except ImportError:
|
||||
import mock
|
||||
from senpy.extensions import Senpy
|
||||
from flask import Flask
|
||||
from flask.ext.testing import TestCase
|
||||
import unittest
|
||||
|
||||
class vaderTest(unittest.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
self.app = Flask("test_plugin")
|
||||
self.dir = os.path.join(os.path.dirname(__file__))
|
||||
self.senpy = Senpy(plugin_folder=self.dir, default_plugins=False)
|
||||
self.senpy.init_app(self.app)
|
||||
|
||||
def tearDown(self):
|
||||
self.senpy.deactivate_plugin("vaderSentiment", sync=True)
|
||||
|
||||
def test_analyse(self):
|
||||
plugin = self.senpy.plugins["vaderSentiment"]
|
||||
plugin.activate()
|
||||
|
||||
texts = {'I am tired :(' : 'marl:Negative',
|
||||
'I love pizza' : 'marl:Positive',
|
||||
'I like going to the cinema :)' : 'marl:Positive',
|
||||
'This cake is disgusting' : 'marl:Negative'}
|
||||
|
||||
for text in texts:
|
||||
response = plugin.analyse(input=text)
|
||||
expected = texts[text]
|
||||
sentimentSet = response.entries[0].sentiments
|
||||
|
||||
max_sentiment = max(sentimentSet, key=lambda x: x['marl:polarityValue'])
|
||||
assert max_sentiment['marl:hasPolarity'] == expected
|
||||
|
||||
plugin.deactivate()
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
@ -5,15 +5,37 @@ from senpy.plugins import SentimentPlugin, SenpyPlugin
|
||||
from senpy.models import Results, Sentiment, Entry
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
class vaderSentimentPlugin(SentimentPlugin):
|
||||
class VaderSentimentPlugin(SentimentPlugin):
|
||||
'''
|
||||
Sentiment classifier using vaderSentiment module. Params accepted: Language: {en, es}. The output uses Marl ontology developed at GSI UPM for semantic web.
|
||||
'''
|
||||
name = "sentiment-vader"
|
||||
module = "sentiment-vader"
|
||||
author = "@icorcuera"
|
||||
version = "0.1.1"
|
||||
extra_params = {
|
||||
"language": {
|
||||
"@id": "lang_rand",
|
||||
"aliases": ["language", "l"],
|
||||
"default": "auto",
|
||||
"options": ["es", "en", "auto"]
|
||||
},
|
||||
|
||||
def analyse_entry(self,entry,params):
|
||||
"aggregate": {
|
||||
"aliases": ["aggregate","agg"],
|
||||
"options": ["true", "false"],
|
||||
"default": False
|
||||
}
|
||||
|
||||
logger.debug("Analysing with params {}".format(params))
|
||||
}
|
||||
requirements = {}
|
||||
|
||||
text_input = entry.get("text", None)
|
||||
def analyse_entry(self, entry, params):
|
||||
|
||||
self.log.debug("Analysing with params {}".format(params))
|
||||
|
||||
text_input = entry.text
|
||||
aggregate = params['aggregate']
|
||||
|
||||
score = sentiment(text_input)
|
||||
@ -22,14 +44,17 @@ class vaderSentimentPlugin(SentimentPlugin):
|
||||
marl__hasPolarity= "marl:Positive",
|
||||
marl__algorithmConfidence= score['pos']
|
||||
)
|
||||
opinion0.prov(self)
|
||||
opinion1 = Sentiment(id= "Opinion_negative",
|
||||
marl__hasPolarity= "marl:Negative",
|
||||
marl__algorithmConfidence= score['neg']
|
||||
)
|
||||
opinion1.prov(self)
|
||||
opinion2 = Sentiment(id= "Opinion_neutral",
|
||||
marl__hasPolarity = "marl:Neutral",
|
||||
marl__algorithmConfidence = score['neu']
|
||||
)
|
||||
opinion2.prov(self)
|
||||
|
||||
if aggregate == 'true':
|
||||
res = None
|
||||
@ -47,3 +72,25 @@ class vaderSentimentPlugin(SentimentPlugin):
|
||||
entry.sentiments.append(opinion2)
|
||||
|
||||
yield entry
|
||||
|
||||
test_cases = []
|
||||
|
||||
test_cases = [
|
||||
{
|
||||
'input': 'I am tired :(',
|
||||
'polarity': 'marl:Negative'
|
||||
},
|
||||
{
|
||||
'input': 'I love pizza :(',
|
||||
'polarity': 'marl:Positive'
|
||||
},
|
||||
{
|
||||
'input': 'I enjoy going to the cinema :)',
|
||||
'polarity': 'marl:Negative'
|
||||
},
|
||||
{
|
||||
'input': 'This cake is disgusting',
|
||||
'polarity': 'marl:Negative'
|
||||
},
|
||||
|
||||
]
|
||||
Loading…
Reference in New Issue
Block a user