class TestPatternSentiment(unittest.TestCase): def setUp(self): self.analyzer = PatternAnalyzer() def test_kind(self): assert_equal(self.analyzer.kind, CONTINUOUS) def test_analyze(self): p1 = "I feel great this morning." n1 = "This is a terrible car." p1_result = self.analyzer.analyze(p1) n1_result = self.analyzer.analyze(n1) assert_true(p1_result[0] > 0) assert_true(n1_result[0] < 0) assert_equal(p1_result.polarity, p1_result[0]) assert_equal(p1_result.subjectivity, p1_result[1]) def test_analyze_assessments(self): p1 = "I feel great this morning." n1 = "This is a terrible car." p1_result = self.analyzer.analyze(p1, keep_assessments=True) n1_result = self.analyzer.analyze(n1, keep_assessments=True) p1_assessment = p1_result.assessments[0] n1_assessment = n1_result.assessments[0] assert_true(p1_assessment[1] > 0) assert_true(n1_assessment[1] < 0) assert_equal(p1_result.polarity, p1_assessment[1]) assert_equal(p1_result.subjectivity, p1_assessment[2])
class TestPatternSentiment(unittest.TestCase): def setUp(self): self.analyzer = PatternAnalyzer() def test_kind(self): assert_equal(self.analyzer.kind, CONTINUOUS) def test_analyze(self): p1 = "I feel great this morning." n1 = "This is a terrible car." p1_result = self.analyzer.analyze(p1) n1_result = self.analyzer.analyze(n1) assert_true(p1_result[0] > 0) assert_true(n1_result[0] < 0) assert_equal(p1_result.polarity, p1_result[0]) assert_equal(p1_result.subjectivity, p1_result[1]) def test_analyze_assessments(self): p1 = "I feel great this morning." n1 = "This is a terrible car." p1_result = self.analyzer.analyze(p1,keep_assessments=True) n1_result = self.analyzer.analyze(n1,keep_assessments=True) p1_assessment = p1_result.assessments[0] n1_assessment = n1_result.assessments[0] assert_true(p1_assessment[1] > 0) assert_true(n1_assessment[1] < 0) assert_equal(p1_result.polarity, p1_assessment[1]) assert_equal(p1_result.subjectivity, p1_assessment[2])
class TestPatternSentiment(unittest.TestCase): def setUp(self): self.analyzer = PatternAnalyzer() def test_kind(self): assert_equal(self.analyzer.kind, CONTINUOUS) def test_analyze(self): p1 = "I feel great this morning." n1 = "This is a terrible car." assert_true(self.analyzer.analyze(p1)[0] > 0) assert_true(self.analyzer.analyze(n1)[0] < 0)
class TestPatternSentiment(unittest.TestCase): def setUp(self): self.analyzer = PatternAnalyzer() def test_kind(self): assert_equal(self.analyzer.kind, CONTINUOUS) def test_analyze(self): p1 = "I feel great this morning." n1 = "This is a terrible car." assert_true(self.analyzer.analyze(p1)[0] > 0) assert_true(self.analyzer.analyze(n1)[0] < 0)
def test_can_get_subjectivity_and_polarity_with_different_analyzer(self): blob = tb.TextBlob("I love this car.", analyzer=NaiveBayesAnalyzer()) pattern = PatternAnalyzer() assert_equal(blob.polarity, pattern.analyze(str(blob))[0]) assert_equal(blob.subjectivity, pattern.analyze(str(blob))[1])
def test_can_get_subjectivity_and_polarity_with_different_analyzer(self): blob = tb.TextBlob("I love this car.", analyzer=NaiveBayesAnalyzer()) pattern = PatternAnalyzer() assert_equal(blob.polarity, pattern.analyze(str(blob))[0]) assert_equal(blob.subjectivity, pattern.analyze(str(blob))[1])
def sentiment(tweet): #return textblob.TextBlob(tweet).sentiment.polarity sentiment_analyzer = PatternAnalyzer() return sentiment_analyzer.analyze(tweet).polarity
self.ax.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kw) def fill(self, data, *args, **kw): sdata = _scale_data(data, self.ranges) self.ax.fill(self.angle, np.r_[sdata, sdata[0]], *args, **kw) if __name__ == "__main__": start = timeit.default_timer() # init the analyzers analyzerBayes = NaiveBayesAnalyzer() analyzerPattern = PatternAnalyzer() #training first resultBayes = analyzerBayes.analyze("train this") resultPattern = analyzerPattern.analyze("train this") sc = SparkContext(appName="MovieSentiment") # map reduce lines = sc.textFile("movieData.txt") posNneg = lines.map(sentimentAnalysis) \ .reduceByKey(lambda a, b: (a[0] + b[0], a[1] + b[1])) output = posNneg.collect() ScoreDict = {} for (feature, posAndNeg) in output: print(feature, posAndNeg)
order="relevance", textFormat="plainText", videoId=mat2) res = req1.execute() # print(res) bags = [] for item in res['items']: comment = item['snippet']['topLevelComment']['snippet']['textDisplay'] # print(comment) bags.append(comment) comments = '' sentences = comments.join(bags) sentiment_analyzer = PatternAnalyzer() # print(sentences) # print(sentiment_analyzer.analyze(sentences)) vv = sentiment_analyzer.analyze(sentences) vv1 = vv.polarity # print(vv1) if vv1 > 0.5: print("Positive") # print(bags) # if bags == item.values(): # # print(bags['textDisplay']) # comments = '' # sentences = comments.join(bags) # print(sentences) # sentiment_analyzer = PatternAnalyzer() # print(sentiment_analyzer.analyze(sentences)) # blob = TextBlob(sentences) # for sentence in blob.sentences: