def test_modality(self):
# Assert -1.0 => +1.0 representing the degree of certainty.
v = en.modality(en.Sentence(en.parse("I wish it would stop raining.")))
self.assertTrue(v < 0)
v = en.modality(
en.Sentence(en.parse("It will surely stop raining soon.")))
self.assertTrue(v > 0)
# Assert the accuracy of the modality algorithm.
# Given are the scores for the CoNLL-2010 Shared Task 1 Wikipedia uncertainty data:
# http://www.inf.u-szeged.hu/rgai/conll2010st/tasks.html#task1
# The baseline should increase (not decrease) when the algorithm is modified.
from pattern.db import Datasheet
from pattern.metrics import test
sentences = []
for certain, sentence in Datasheet.load(
os.path.join(PATH, "corpora", "uncertainty-conll2010.csv")):
sentence = en.parse(sentence, chunks=False, light=True)
sentence = en.Sentence(sentence)
sentences.append((sentence, int(certain) > 0))
A, P, R, F = test(lambda sentence: en.modality(sentence) > 0.5,
sentences)
#print A, P, R, F
self.assertTrue(A > 0.69)
self.assertTrue(P > 0.71)
self.assertTrue(R > 0.64)
self.assertTrue(F > 0.67)
print "pattern.en.modality()"
def test_modality(self):
# Assert -1.0 => +1.0 representing the degree of certainty.
v = en.modality(en.Sentence(en.parse("I wish it would stop raining.")))
self.assertTrue(v < 0)
v = en.modality(
en.Sentence(en.parse("It will surely stop raining soon.")))
self.assertTrue(v > 0)
# Assert the accuracy of the modality algorithm.
# Given are the scores for the CoNLL-2010 Shared Task 1 Wikipedia uncertainty data:
# http://www.inf.u-szeged.hu/rgai/conll2010st/tasks.html#task1
# The baseline should increase (not decrease) when the algorithm is
# modified.
from pattern.db import Datasheet
from pattern.metrics import test
sentences = []
for certain, sentence in Datasheet.load(os.path.join(PATH, "corpora", "uncertainty-conll2010.csv")):
sentence = en.parse(sentence, chunks=False, light=True)
sentence = en.Sentence(sentence)
sentences.append((sentence, int(certain) > 0))
A, P, R, F = test(
lambda sentence: en.modality(sentence) > 0.5, sentences)
#print(A, P, R, F)
self.assertTrue(A > 0.69)
self.assertTrue(P > 0.72)
self.assertTrue(R > 0.64)
self.assertTrue(F > 0.68)
print("pattern.en.modality()")
def getData(self, params):
if self.now_cache is not None:
if (self.now_cache + datetime.timedelta(minutes=5)) < datetime.datetime.now():
self.data_cache = None
self.today_cache = None
self.now_cache = None
if self.data_cache is None:
tweets = []
for cand in candidates:
tweets.append({'tweets': api.user_timeline(cand['user'], count=20),
'name': cand['name'],
'party': cand['party']})
all_tweets = []
for tweet_data in tweets:
name = tweet_data['name']
party = tweet_data['party']
for tweet in tweet_data['tweets']:
all_tweets.append( {'Name': name,
'Tweet': tweet.text,
'Favorites': tweet.favorite_count,
'Retweets': tweet.retweet_count} )
dfs = pd.DataFrame(all_tweets)
sentiments = [sentiment(tweet) for tweet in dfs['Tweet']]
dfs['Polarity'] = [sent[0] for sent in sentiments]
dfs['Subjectivity'] = [sent[1] for sent in sentiments]
modal = [modality(Sentence(parse(tweet, lemmata=True))) for tweet in dfs['Tweet']]
dfs['Certainty'] = modal
today = date.strftime(datetime.datetime.now(), format='%m/%d/%Y, %H:%M')
now = datetime.datetime.now()
self.data_cache = dfs
self.today_cache = today
self.now_cache = now
return self.data_cache
def add_modality(tdb):
for tweet in tdb:
s = parse(tweet[2], lemmata=True)
s = Sentence(s)
(form, score) = (mood(s), modality(s))
tweet.extend((form, score))
return tdb
def team_sentiment_analysis(stats):
for s in stats.sentences:
this_sentiment = sentiment(s)
polarity = float("{0:.2f}".format(this_sentiment[0]))
subjectivity = float("{0:.2f}".format(this_sentiment[1]))
polarity_10 = float("{0:.1f}".format(this_sentiment[0]))
subjectivity_10 = float("{0:.1f}".format(this_sentiment[1]))
stats.polarity_counts[polarity] += 1
stats.subjectivity_counts[subjectivity] += 1
stats.polarity_counts_10s[polarity_10] += 1
stats.subjectivity_counts_10s[subjectivity_10] += 1
s = Sentence(parse(s, lemmata=True))
stats.mood_counts[mood(s)] += 1
rounded_modality = float("{0:.2f}".format(modality(s)))
rounded_modality_10 = float("{0:.1f}".format(modality(s)))
stats.modality_counts[rounded_modality] += 1
stats.modality_counts_10s[rounded_modality_10] += 1
def getData(self, params):
if self.now_cache is not None:
if (self.now_cache +
datetime.timedelta(minutes=5)) < datetime.datetime.now():
self.data_cache = None
self.today_cache = None
self.now_cache = None
if self.data_cache is None:
tweets = []
for cand in candidates:
tweets.append({
'tweets':
api.user_timeline(cand['user'], count=20),
'name':
cand['name'],
'party':
cand['party']
})
all_tweets = []
for tweet_data in tweets:
name = tweet_data['name']
party = tweet_data['party']
for tweet in tweet_data['tweets']:
all_tweets.append({
'Name': name,
'Tweet': tweet.text,
'Favorites': tweet.favorite_count,
'Retweets': tweet.retweet_count
})
dfs = pd.DataFrame(all_tweets)
sentiments = [sentiment(tweet) for tweet in dfs['Tweet']]
dfs['Polarity'] = [sent[0] for sent in sentiments]
dfs['Subjectivity'] = [sent[1] for sent in sentiments]
modal = [
modality(Sentence(parse(tweet, lemmata=True)))
for tweet in dfs['Tweet']
]
dfs['Certainty'] = modal
today = date.strftime(datetime.datetime.now(),
format='%m/%d/%Y, %H:%M')
now = datetime.datetime.now()
self.data_cache = dfs
self.today_cache = today
self.now_cache = now
return self.data_cache
def calculate_phrase_sentiment(self, phrases):
# print "Rating phrases sentiment..."
valence_list = []
arousal_list = []
for p in phrases:
pol = sentiment(p)[0]
sent = parse(p, lemmata=True)
mod = modality(Sentence(sent))
print mod
valence_list.append(10 * pol)
arousal_list.append(5 * mod)
valence = max(valence_list)
arousal = max(arousal_list)
print "Valence: " + str(valence)
print "arousal: " + str(arousal)
return ((valence, arousal))
def messages():
#sorts friends by sentiment and modality of their last message to you. Returns rankings as "Friends' Happiness" and "Friends' Confidence"
graph = GraphAPI(token)
me = f.profile()
happiness = {}
confidence = {}
snippets = graph.fql('SELECT snippet, snippet_author FROM thread WHERE folder_id = 0 OR folder_id = 1 Limit 10000',3)
#the above code was heavily influenced by arofcoding.blogspot.com/2012/10/python-script-to-fetch-messages-from.html
#returns a dictionary of message snippets along with the corresponding facebook friend IDs
for dictionary in snippets['data']:
#puts snippets in a dictionary where each author is mapped to the sentiment of their message
happiness[sentiment(dictionary['snippet'])] = dictionary['snippet_author']
confidence[modality(dictionary['snippet'])] = dictionary['snippet_author']
#ranks dictionary entries by positivity of sentiment
happiness_rankings = rank(happiness)
confidence_rankings = rank(confidence)
print "Friends' Happiness (low to high):"
print happiness_rankings
print "Friends' Confidence (low to high):"
print confidence_rankings
def transform(self, text_fields):
stats = []
punctuation = string.punctuation
abvs = ['CNN', 'FBI', 'ABC', 'MSNBC', 'GOP', 'U.S.', 'US', 'ISIS', 'DNC', 'TV', 'CIA',
'I', 'AP', 'PM', 'AM', 'EU', 'USA', 'UK', 'UN', 'CEO', 'NASA', 'LGBT', 'LGBTQ', 'NAFTA', 'ACLU']
for field in text_fields:
field_stats = {}
tok_text = nltk.word_tokenize(field)
try:
num_upper = float(len([w for w in tok_text if w.isupper() and w not in abvs]))/len(tok_text)
except:
num_upper = 0
try:
num_punct = float(len([ch for ch in field if ch in punctuation]))/len(field)
except:
num_punct = 0
try:
sent_lengths = [len(nltk.word_tokenize(s)) for s in nltk.sent_tokenize(field)]
av_sent_len = float(sum(sent_lengths))/len(sent_lengths)
except:
av_sent_len = 0
try:
num_prof = float(len([w for w in tok_text if w.lower() in PROFANITY]))/len(tok_text)
except:
num_prof = 0
mood = modality(field)
polarity, subjectivity = sentiment(field)
field_stats['all_caps'] = num_upper
field_stats['sent_len'] = av_sent_len
field_stats['polarity'] = polarity
field_stats['subjectivity'] = subjectivity
field_stats['profanity'] = num_prof
field_stats['mood'] = mood
stats.append(field_stats)
return stats
print 'Review:'
print review
print
print 'Labeled Sentiment:', review_sentiment
print
final_sentiment = analyze_sentiment_pattern_lexicon(review,
threshold=0.1,
verbose=True)
print '-'*60
for review, review_sentiment in sample_data:
print 'Review:'
print review
print 'Labeled Sentiment:', review_sentiment
print 'Mood:', mood(review)
mod_score = modality(review)
print 'Modality Score:', round(mod_score, 2)
print 'Certainty:', 'Strong' if mod_score > 0.5 \
else 'Medium' if mod_score > 0.35 \
else 'Low'
print '-'*60
pattern_predictions = [analyze_sentiment_pattern_lexicon(review, threshold=0.1)
for review in test_reviews]
def process(self, message):
# print pattern_en.suggest(message) -- suggestions
if message == ">!train":
self.train()
return "It is nice to learn new stuff."
if message == ">!forget":
memory.clear()
return "I am reborn. So much free space :) maybe you will use files to store memory and not RAM..."
if message == ">!load_page":
if sessionId not in memory:
response = "Hello! My name is Chad and I am passionate about music."
response += "We can share our experiences and maybe we can get along."
response += "Would you mind telling me your name first?"
expect[sessionId] = "name"
memory[sessionId] = dict()
else:
response = "Welcome back!"
search.search("new songs")
with open('results.json') as data_file:
data = json.load(data_file)
for i in range(10):
if 'musicrecording' in data['items'][i]['pagemap']:
mr = data['items'][i]['pagemap']['musicrecording']
which = random.randint(0, len(mr) - 1)
if 'name' not in mr[which]:
response += " Did you know that " + mr[which][
'byartist'] + " has released a new song?"
else:
response += " You can check out this cool song, " + mr[which]['name'] + ", by " + \
mr[which]['byartist']
return response
s = nlp.get_sentences(message)
doc = spacy_nlp(message)
for w in doc:
print "(", w, w.dep_, w.pos_, w.head, ")"
aiml_sent_type = []
aiml_responses = []
memory_responses = []
sentence_types = []
emotions = []
for sentence in s:
sentence_type = self.instant_classifier.classify(
dialogue_act_features(sentence))
sentence_types.append(sentence_type)
polarity, subjective = pattern_en.sentiment(sentence)
sent = pattern_en.parse(sentence, lemmata=True)
sent = pattern_en.Sentence(sent)
modality = pattern_en.modality(sent)
mood = pattern_en.mood(sent)
if polarity > 0.8:
emotions.append("SUPER HAPPY")
elif polarity > 0.3:
emotions.append("GOOD SURPRISE")
elif polarity < -0.4:
emotions.append("FEAR")
elif polarity > 0.4:
emotions.append("COOL")
elif polarity < -0.1:
emotions.append("SAD")
elif polarity < -0.7:
emotions.append("ANGER")
else:
emotions.append("NEUTER")
print sentence_type, polarity, subjective, modality, mood
if sentence_type not in ["whQuestion", "ynQuestion"]:
try:
aiml_sent_type_res = self.kernel.respond(
sentence_type, sessionId)
except:
aiml_sent_type_res = ""
aiml_sent_type.append(aiml_sent_type_res)
verbs_subj = set()
sentence = sentence[0].upper() + sentence[1:]
doc = spacy_nlp(sentence)
for possible_subject in doc:
if (possible_subject.dep == nsubj or possible_subject.dep
== nsubjpass) and possible_subject.head.pos == VERB:
verbs_subj.add((possible_subject, possible_subject.head))
try:
aiml_response = self.kernel.respond(sentence, sessionId)
except:
aiml_response = ""
aiml_responses.append(aiml_response)
# MEMORY MODULE
memory_msg = ""
if sentence_type == "Statement":
# insert into memory
for i in verbs_subj:
subjs = []
subjects = [i[0]]
for tok in i[0].children:
if tok.dep == conj:
subjects.append(tok)
for subj in subjects:
predec = ""
for tok in subj.children:
if tok.dep_ == "poss" or tok.dep == amod:
predec += tok.lower_
if len(predec) > 0:
subjs.append(predec + " " + subj.lower_)
else:
subjs.append(subj.lower_)
vb = i[1].lower_
if vb not in memory[sessionId]:
memory[sessionId][vb] = dict()
for subj in subjs:
for c in i[1].children:
if c.dep in [prep]:
memory[sessionId][vb][subj] = c.lower_ + " "
for c_prep in c.children:
if c_prep.dep in [dobj, pobj, attr]:
memory[sessionId][vb][
subj] += c_prep.text
memory_responses.append(
self.kernel.respond(
"memorate", sessionId))
elif c.dep in [dobj, pobj, attr]:
memory[sessionId][vb][subj] = c.text
memory_responses.append(
self.kernel.respond("memorate", sessionId))
elif sentence_type == "whQuestion":
for i in verbs_subj:
subjs = []
subjects = [i[0]]
for tok in i[0].children:
if tok.dep == conj:
subjects.append(tok)
for subj in subjects:
predec = ""
for tok in subj.children:
if tok.dep_ == "poss" or tok.dep == amod:
predec += tok.lower_
if len(predec) > 0:
subjs.append(predec + " " + subj.lower_)
else:
subjs.append(subj.lower_)
max_similarity = 0
verb = i[1].lower_
for j in memory[sessionId]:
p_word = spacy_nlp(j)
similarity = i[1].similarity(p_word[0])
if similarity > max_similarity:
max_similarity = similarity
verb = j
if max_similarity > 0.5 and verb in memory[sessionId]:
num_subjs = len(subjs)
memory_msg = ""
for subj in subjs:
if subj in memory[sessionId][verb]:
toks = nlp.tokenize_text(subj)
memory_msg = ""
for t in toks:
if t in first_person:
memory_msg += pron_translate[t] + " "
else:
memory_msg += t + " "
num_subjs -= 1
if num_subjs > 2:
memory_msg += ", "
elif num_subjs == 1:
memory_msg += "and "
if len(memory_msg) > 0:
memory_msg += verb + " "
if num_subjs != len(subjs):
memory_msg += memory[sessionId][verb][
subjs[-1]] + "."
memory_responses.append(memory_msg)
arr_response = []
for i in aiml_sent_type:
if len(i) > 0:
arr_response.append(i)
for i in aiml_responses:
if len(i) > 0:
arr_response.append(i)
for i in memory_responses:
if len(i) > 0:
arr_response.append(i)
if len(arr_response) == 0:
data = search.search(message)
snip = data['items'][0]['snippet']
sents = nlp.get_sentences(snip)
arr_response.append(sents[0])
response = ""
for i in emotions:
try:
emoi = self.kernel.respond(i, sessionId)
except:
emoi = None
if emoi is not None:
if random.randint(0, 100) < 50:
response += " " + emoi + "."
break
for res in arr_response:
if len(res) > 1:
response += res + " "
# generic response, if no response
restoks = nlp.tokenize_text(response)
if len(restoks) == 0:
idx = random.randint(0, len(sentence_types) - 1)
try:
aiml_response = self.kernel.respond(sentence_types[idx],
sessionId)
except:
aiml_response = ""
response += aiml_response
# polarity, subjective = pattern_en.sentiment(response)
# sent = pattern_en.parse(sentence, lemmata=True)
# sent = pattern_en.Sentence(sent)
# modality = pattern_en.modality(sent)
# mood = pattern_en.mood(sent)
# sentence_type = self.instant_classifier.classify(dialogue_act_features(response))
# print response, polarity, subjective, modality, mood
return response
def extract_bias_features(text):
features = {}
txt_lwr = str(text).lower()
words = nltk.word_tokenize(txt_lwr)
words = [w for w in words if len(w) > 0 and w not in '.?!,;:\'s"$']
if len(words) < 1:
return None
unigrams = sorted(list(set(words)))
bigram_tokens = nltk.bigrams(words)
bigrams = [" ".join([w1, w2]) for w1, w2 in sorted(set(bigram_tokens))]
trigram_tokens = nltk.trigrams(words)
trigrams = [" ".join([w1, w2, w3]) for w1, w2, w3 in sorted(set(trigram_tokens))]
# print words
# print unigrams
# print bigrams
# print trigrams
# print "----------------------"
# word count
features['word_count'] = float(len(words))
# unique word count
features['unique_word_count'] = float(len(unigrams))
# coherence marker count
count, instances = count_feature_list_freq(coherence, words, bigrams, trigrams)
# if count > 0:
features['coherence_marker_count'] = count
features['coherence_marker_prop'] = round(float(count) / float(len(words)), 4)
features['coherence_marker_list'] = instances
# degree modifier count
count, instances = count_feature_list_freq(modifiers, words, bigrams, trigrams)
#if count > 0:
features['degree_modifier_count'] = count
features['degree_modifier_prop'] = round(float(count) / float(len(words)), 4)
features['degree_modifier_list'] = instances
# hedge word count
count, instances = count_feature_list_freq(hedges, words, bigrams, trigrams)
#if count > 0:
features['hedge_word_count'] = count
features['hedge_word_prop'] = round(float(count) / float(len(words)), 4)
features['hedge_word_list'] = instances
# factive verb count
count, instances = count_feature_list_freq(factives, words, bigrams, trigrams)
#if count > 0:
features['factive_verb_count'] = count
features['factive_verb_prop'] = round(float(count) / float(len(words)), 4)
features['factive_verb_list'] = instances
# assertive verb count
count, instances = count_feature_list_freq(assertives, words, bigrams, trigrams)
#if count > 0:
features['assertive_verb_count'] = count
features['assertive_verb_prop'] = round(float(count) / float(len(words)), 4)
features['assertive_verb_list'] = instances
# implicative verb count
count, instances = count_feature_list_freq(implicatives, words, bigrams, trigrams)
#if count > 0:
features['implicative_verb_count'] = count
features['implicative_verb_prop'] = round(float(count) / float(len(words)), 4)
features['implicative_verb_list'] = instances
# bias words and phrases count
count, instances = count_feature_list_freq(biased, words, bigrams, trigrams)
#if count > 0:
features['bias_count'] = count
features['bias_prop'] = round(float(count) / float(len(words)), 4)
features['bias_list'] = instances
# opinion word count
count, instances = count_feature_list_freq(opinionLaden, words, bigrams, trigrams)
#if count > 0:
features['opinion_count'] = count
features['opinion_prop'] = round(float(count) / float(len(words)), 4)
features['opinion_list'] = instances
# weak subjective word count
count, instances = count_feature_list_freq(subj_weak, words, bigrams, trigrams)
#if count > 0:
features['subjective_weak_count'] = count
features['subjective_weak_prop'] = round(float(count) / float(len(words)), 4)
features['subjective_weak_list'] = instances
# strong subjective word count
count, instances = count_feature_list_freq(subj_strong, words, bigrams, trigrams)
#if count > 0:
features['subjective_strong_count'] = count
features['subjective_strong_prop'] = round(float(count) / float(len(words)), 4)
features['subjective_strong_list'] = instances
# composite sentiment score using VADER sentiment analysis package
compound_sentiment = vader_sentiment_analysis.polarity_scores(text)['compound']
features['vader_composite_sentiment'] = float(compound_sentiment)
# subjectivity score using Pattern.en
pattern_subjectivity = pattern_sentiment(text)[1]
features['subjectivity_score'] = round(pattern_subjectivity, 4)
# modality (certainty) score and mood using http://www.clips.ua.ac.be/pages/pattern-en#modality
sentence = parse(text, lemmata=True)
sentenceObj = Sentence(sentence)
features['modality'] = round(modality(sentenceObj), 4)
try:
features['mood'] = mood(sentenceObj)
except IndexError as e:
print "IndexError: %s" % e
print "Ignoring..."
features['mood'] = 'err'
# Flesch-Kincaid Grade Level (reading difficulty) using textstat
try:
features['flesch-kincaid_grade_level'] = float(textstat.flesch_kincaid_grade(text))
except TypeError as e:
print "TypeError: %s" % e
print "Ignoring..."
features['flesch-kincaid_grade_level'] = 0.0
# liwc 3rd person pronoun count (combines S/he and They)
count, instances = count_liwc_list_freq(liwc_3pp, words)
#if count > 0:
features['liwc_3rd_person_pronoum_count'] = count
features['liwc_3rd_person_pronoun_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_3rd_person_pronoun_list'] = instances
# liwc auxiliary verb count
count, instances = count_liwc_list_freq(liwc_aux, words)
#if count > 0:
features['liwc_auxiliary_verb_count'] = count
features['liwc_auxiliary_verb_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_auxiliary_verb_list'] = instances
# liwc adverb count
count, instances = count_liwc_list_freq(liwc_adv, words)
#if count > 0:
features['liwc_adverb_count'] = count
features['liwc_adverb_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_adverb_list'] = instances
# liwc preposition count
count, instances = count_liwc_list_freq(liwc_prep, words)
#if count > 0:
features['liwc_preposition_count'] = count
features['liwc_preposition_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_preposition_list'] = instances
# liwc conjunction count
count, instances = count_liwc_list_freq(liwc_conj, words)
#if count > 0:
features['liwc_conjunction_count'] = count
features['liwc_conjunction_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_conjunction_list'] = instances
# liwc discrepency word count
count, instances = count_liwc_list_freq(liwc_discr, words)
#if count > 0:
features['liwc_discrepency_word_count'] = count
features['liwc_discrepency_word_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_discrepency_word_list'] = instances
# liwc tentative word count
count, instances = count_liwc_list_freq(liwc_tent, words)
#if count > 0:
features['liwc_tentative_word_count'] = count
features['liwc_tentative_word_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_tentative_word_list'] = instances
# liwc certainty word count
count, instances = count_liwc_list_freq(liwc_cert, words)
#if count > 0:
features['liwc_certainty_word_count'] = count
features['liwc_certainty_word_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_certainty_word_list'] = instances
# liwc causation word count
count, instances = count_liwc_list_freq(liwc_causn, words)
#if count > 0:
features['liwc_causation_word_count'] = count
features['liwc_causation_word_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_causation_word_list'] = instances
# liwc work word count
count, instances = count_liwc_list_freq(liwc_work, words)
#if count > 0:
features['liwc_work_word_count'] = count
features['liwc_work_word_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_work_word_list'] = instances
# liwc achievement word count
count, instances = count_liwc_list_freq(liwc_achiev, words)
#if count > 0:
features['liwc_achievement_word_count'] = count
features['liwc_achievement_word_prop'] = round(float(count) / float(len(words)), 4)
features['liwc_achievement_word_list'] = instances
return features
type=None,
role=None,
relation=None)
print pnp.string # String of words (Unicode).
print pnp.chunks # List of Chunk objects.
# print pnp.preposition # First PP chunk in the PNP.
# sentiment
print sentiment(
"The movie attempts to be surreal by incorporating various time paradoxes,"
"but it's presented in such a ridiculous way it's seriously boring.")
print sentiment('Wonderfully awful! :-)').assessments
# mode and modality
s = "Some amino acids tend to be acidic while others may be basic." # weaseling
s = parse(s, lemmata=True)
s = Sentence(s)
print modality(s)
# wordnet
s = wordnet.synsets('bird')[0]
print 'Definition:', s.gloss # Definition string.
print ' Synonyms:', s.synonyms # List of word forms (i.e., synonyms)
print ' Hypernyms:', s.hypernyms(
) # returns a list of parent synsets (i.e., more general). Synset (semantic parent).
print ' Hypernyms:', s.hypernyms(recursive=False, depth=None)
print ' Hyponyms:', s.hyponyms(
) # returns a list child synsets (i.e., more specific).
print ' Hyponyms:', s.hyponyms(recursive=False, depth=None)
print ' Holonyms:', s.holonyms(
) # List of synsets (of which this is a member).
print ' Meronyms:', s.meronyms() # List of synsets (members/parts).
print ' POS:', s.pos # Part-of-speech: NOUN | VERB | ADJECTIVE | ADVERB.
print ' Category:', s.lexname # Category string, or None.
from docs import TEST_DOCUMENTS
for doc in TEST_DOCUMENTS:
sentences = doc['sentences']
conditionals = 0
indicatives = 0
imperatives = 0
subjunctives = 0
minModality = 1
maxModality = -1
for sentence in sentences:
s = parse(sentence, lemmata=True)
s = Sentence(s)
m = mood(s)
modal = modality(s)
#set the max or min value
if modal > maxModality:
maxModality = modal
if modal < minModality:
minModality = modal
#this count moods
if m is "conditional":
conditionals = conditionals + 1
elif m is "indicative":
indicatives = indicatives + 1
elif m is "imperative":
imperatives = imperatives + 1
elif m is "subjunctive":
subjunctives = subjunctives + 1
writer.writerow({
# Explanation:
#
# - 0.75 show the sentiment score of the sentence that means highly positive
# - 0.8 is the subjectivity score that is a personal of the user
# ### Checking if a Statement is a Fact
from pattern.en import parse, Sentence
from pattern.en import modality
text = "Paris is the capital of France"
sent = parse(text, lemmata=True)
sent = Sentence(sent)
print(modality(sent))
text = "I think we can complete this task"
sent = parse(text, lemmata=True)
sent = Sentence(sent)
print(modality(sent))
# ### Spelling Corrections
from pattern.en import suggest
print(suggest("Whitle"))
from pattern.en import suggest
print(suggest("Fracture"))
def extract_bias_features(text):
features = {}
text = unicode(text, errors='ignore')
txt_lwr = str(text).lower()
words = nltk.word_tokenize(txt_lwr)
words = [w for w in words if len(w) > 0 and w not in '.?!,;:\'s"$']
unigrams = sorted(list(set(words)))
bigram_tokens = nltk.bigrams(words)
bigrams = [" ".join([w1, w2]) for w1, w2 in sorted(set(bigram_tokens))]
trigram_tokens = nltk.trigrams(words)
trigrams = [
" ".join([w1, w2, w3]) for w1, w2, w3 in sorted(set(trigram_tokens))
]
# print words
# print unigrams
# print bigrams
# print trigrams
# print "----------------------"
# word count
features['word_cnt'] = len(words)
# unique word count
features['unique_word_cnt'] = len(unigrams)
# coherence marker count
count = count_feature_list_freq(coherence, words, bigrams, trigrams)
features['cm_cnt'] = count
features['cm_rto'] = round(float(count) / float(len(words)), 4)
# degree modifier count
count = count_feature_list_freq(modifiers, words, bigrams, trigrams)
features['dm_cnt'] = count
features['dm_rto'] = round(float(count) / float(len(words)), 4)
# hedge word count
count = count_feature_list_freq(hedges, words, bigrams, trigrams)
features['hedge_cnt'] = count
features['hedge_rto'] = round(float(count) / float(len(words)), 4)
# factive verb count
count = count_feature_list_freq(factives, words, bigrams, trigrams)
features['factive_cnt'] = count
features['factive_rto'] = round(float(count) / float(len(words)), 4)
# assertive verb count
count = count_feature_list_freq(assertives, words, bigrams, trigrams)
features['assertive_cnt'] = count
features['assertive_rto'] = round(float(count) / float(len(words)), 4)
# implicative verb count
count = count_feature_list_freq(implicatives, words, bigrams, trigrams)
features['implicative_cnt'] = count
features['implicative_rto'] = round(float(count) / float(len(words)), 4)
# bias words and phrases count
count = count_feature_list_freq(biased, words, bigrams, trigrams)
features['bias_cnt'] = count
features['bias_rto'] = round(float(count) / float(len(words)), 4)
# opinion word count
count = count_feature_list_freq(opinionLaden, words, bigrams, trigrams)
features['opinion_cnt'] = count
features['opinion_rto'] = round(float(count) / float(len(words)), 4)
# weak subjective word count
count = count_feature_list_freq(subj_weak, words, bigrams, trigrams)
features['subj_weak_cnt'] = count
features['subj_weak_rto'] = round(float(count) / float(len(words)), 4)
# strong subjective word count
count = count_feature_list_freq(subj_strong, words, bigrams, trigrams)
features['subj_strong_cnt'] = count
features['subj_strong_rto'] = round(float(count) / float(len(words)), 4)
# composite sentiment score using VADER sentiment analysis package
compound_sentiment = vader_sentiment_analysis.polarity_scores(
text)['compound']
features['vader_sentiment'] = compound_sentiment
# subjectivity score using Pattern.en
pattern_subjectivity = pattern_sentiment(text)[1]
features['subjectivity'] = round(pattern_subjectivity, 4)
# modality (certainty) score and mood using http://www.clips.ua.ac.be/pages/pattern-en#modality
sentence = parse(text, lemmata=True)
sentenceObj = Sentence(sentence)
features['modality'] = round(modality(sentenceObj), 4)
features['mood'] = mood(sentenceObj)
# Flesch-Kincaid Grade Level (reading difficulty) using textstat
features['fk_gl'] = textstat.flesch_kincaid_grade(text)
# liwc 3rd person pronoun count (combines S/he and They)
count = count_liwc_list_freq(liwc_3pp, words)
features['liwc_3pp_cnt'] = count
features['liwc_3pp_rto'] = round(float(count) / float(len(words)), 4)
# liwc auxiliary verb count
count = count_liwc_list_freq(liwc_aux, words)
features['liwc_aux_cnt'] = count
features['liwc_aux_rto'] = round(float(count) / float(len(words)), 4)
# liwc adverb count
count = count_liwc_list_freq(liwc_adv, words)
features['liwc_adv_cnt'] = count
features['liwc_adv_rto'] = round(float(count) / float(len(words)), 4)
# liwc preposition count
count = count_liwc_list_freq(liwc_prep, words)
features['liwc_prep_cnt'] = count
features['liwc_prep_rto'] = round(float(count) / float(len(words)), 4)
# liwc conjunction count
count = count_liwc_list_freq(liwc_conj, words)
features['liwc_conj_cnt'] = count
features['liwc_conj_rto'] = round(float(count) / float(len(words)), 4)
# liwc discrepency word count
count = count_liwc_list_freq(liwc_discr, words)
features['liwc_discr_cnt'] = count
features['liwc_discr_rto'] = round(float(count) / float(len(words)), 4)
# liwc tentative word count
count = count_liwc_list_freq(liwc_tent, words)
features['liwc_tent_cnt'] = count
features['liwc_tent_rto'] = round(float(count) / float(len(words)), 4)
# liwc certainty word count
count = count_liwc_list_freq(liwc_cert, words)
features['liwc_cert_cnt'] = count
features['liwc_cert_rto'] = round(float(count) / float(len(words)), 4)
# liwc causation word count
count = count_liwc_list_freq(liwc_causn, words)
features['liwc_causn_cnt'] = count
features['liwc_causn_rto'] = round(float(count) / float(len(words)), 4)
# liwc work word count
count = count_liwc_list_freq(liwc_work, words)
features['liwc_work_cnt'] = count
features['liwc_work_rto'] = round(float(count) / float(len(words)), 4)
# liwc achievement word count
count = count_liwc_list_freq(liwc_achiev, words)
features['liwc_achiev_cnt'] = count
features['liwc_achiev_rto'] = round(float(count) / float(len(words)), 4)
return features
def modality_score(sentence):
s = parse(sentence, lemmata=True)
s = Sentence(s)
return modality(s)
print PAST in tenses('purred') # 'p' in tenses() also works.
print (PAST, 1, PL) in tenses('purred')
print 'Quantification'
print quantify(['goose', 'goose', 'duck', 'chicken', 'chicken', 'chicken'])
print quantify('carrot', amount=90)
print quantify({'carrot': 100, 'parrot': 20})
print 'ngrams'
print ngrams("I am eating a pizza.", n=2)
#parse
s = parse('I eat pizza with a fork.')
pprint(s)
#tag
for word, t in tag('The cat felt happy.'):
print word +' is ' +t
s = "The movie attempts to be surreal by incorporating various time paradoxes, but it's presented in such a ridiculous way it's seriously boring."
print sentiment(s)
print polarity(s)
print subjectivity(s)
#The modality() function returns a value between -1.0 and +1.0, expressing the degree of certainty
s2 = "Some amino acids tend to be acidic while others may be basic." # weaseling
se = Sentence(parse(s, chunks=False, lemmata=True))
print modality(se)
dicWord[nn]=1
else:
for w in chunk:
if w.type=="JJ":
index=c.lower().find(w.string)
print index
print w
if index>0 and judge(c,index):
c=c[:index+len(w.string)]+'</span>'+c[index+len(w.string):]
c=c[:index]+'<span class=*JJ* >'+c[index:]
#print c
c='<span class=*sentence* sentiment=*'+str(sentiment(sentence))+'* positive=*'+str(positive(sentence))+'* mood=*'+str(mood(sentence))+'* modality=*'+str(modality(sentence))+'*>'+c+"</span>"
c=c.replace('"','*')
v.texts=v.texts+c
#print c
#pdb.set_trace()
#print v.texts
print v.date
#print v.nouns
#print v.texts
print v.stars
cur.execute('insert into wZwZcte4lcbu51NOzCjWbQ values("'+v.date+'","'+v.user+'","'+v.nouns+'","'+str(v.stars)+'" ,"'+v.texts+'")')
#cur.execute('create table wordfre(word varchar(20) UNIQUE,uid integer)')
cur.close()
cx.commit()
print conjugate('purred', '3sg')
print PAST in tenses('purred') # 'p' in tenses() also works.
print(PAST, 1, PL) in tenses('purred')
print 'Quantification'
print quantify(['goose', 'goose', 'duck', 'chicken', 'chicken', 'chicken'])
print quantify('carrot', amount=90)
print quantify({'carrot': 100, 'parrot': 20})
print 'ngrams'
print ngrams("I am eating a pizza.", n=2)
#parse
s = parse('I eat pizza with a fork.')
pprint(s)
#tag
for word, t in tag('The cat felt happy.'):
print word + ' is ' + t
s = "The movie attempts to be surreal by incorporating various time paradoxes, but it's presented in such a ridiculous way it's seriously boring."
print sentiment(s)
print polarity(s)
print subjectivity(s)
#The modality() function returns a value between -1.0 and +1.0, expressing the degree of certainty
s2 = "Some amino acids tend to be acidic while others may be basic." # weaseling
se = Sentence(parse(s, chunks=False, lemmata=True))
print modality(se)
def checkModality(sentence):
return modality(sentence)
def get_score(self, content: str):
self.sent = parse(content, lemmata=True)
self.sent = Sentence(self.sent)
self.modality = modality(self.sent)
return self.modality
