def score_sentiment(): if len(sys.argv) < 3: print ("Run: python sentimentByTextBlob.py <Input csv file> <Output csv filename>") sys.exit(1) s = TextBlobSentiment(sys.argv[1], sys.argv[2]) #Calculating sentiment based on the patternanalyzer s.calculatePatternAnalyzerSentiments()
rep = rep.append(rep3,ignore_index=True) rep = rep.append(rep4,ignore_index=True) rep = rep.append(rep5,ignore_index=True) rep = rep.append(rep6,ignore_index=True) rep = rep.append(rep7,ignore_index=True) rep = rep.append(rep8,ignore_index=True) rep = rep.append(rep9,ignore_index=True) rep = rep.append(rep10,ignore_index=True) rep = rep.append(rep11,ignore_index=True) rep = rep.append(rep12,ignore_index=True) alldebates = rep.append(dem,ignore_index=True) #Change the order of columns in the pandas dataframe columnsOrder = ['Party','DebateNo','SentenceNo', 'SequenceNo', 'Speaker', 'Text'] alldebates = alldebates.reindex(columns=columnsOrder) alldebates.to_csv("all_debates.csv",index = False) #Calculate Sentiments s = TextBlobSentiment("all_debates.csv", "all_debates_PAsent.csv") #Calculating sentiment based on the patternanalyzer s.calculatePatternAnalyzerSentiments() s = TextBlobSentiment("all_debates.csv", "all_debates_NBsent.csv") #Calculating sentiment based on the patternanalyzer s.calculateNBSentiments()