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()