this is code snippet of sentiment analysis using sentiwordnet in (python using Pandas). i am trying to extract sentiment score of each review using sentiwordnet. here are few steps i did upto now,
1.stopwords removal.
2.tokenisation.
3.pos tagging.
can anyone help me to correct this code. results of this code are shown at the bottom.
import pandas as pd
import numpy as np
import nltk
print("\n\n")
df=pd.read_csv("smallcsvdata.csv")
print("Dataset.................")
print (df)
print("\n\n")
from nltk.tokenize import word_tokenize
from nltk import pos_tag, pos_tag_sents
#stopwords removal////////////////////////
print("lower..............\n\n")
texts = df['Review_Text'].map(lambda Review_Text: Review_Text.lower())#..............................converting to lower case
print(texts)
df['texts']=texts
print("Tokenisation..............\n\n")
token = df.apply(lambda row: nltk.word_tokenize(row['texts']), axis=1)
df['tokenized_words']=token
print(df['tokenized_words'])
print("stopwords removal///////////////////////")
words=df['tokenized_words']
from nltk.corpus import stopwords
#stop_words = set(stopwords.words('english'))
usertext = token
stop_words=['a', 'able','about','across','after','all','almost','also','am','among','an','and','any','are','as','at','be','because','been','by','did','else','ever','every','for','from','get','got','had','has','have','he','her','hers','him','his','how','however','i','if','in','into','is','it','its','just','least','let','may','me','might','my','of','off','on','or','other','our','own','rather','said','say','says','she','should','since','so','than','that','the','their','them','then','there','these','they','this','tis','to','was','us','was','we','were','what','when','where','while','who','whom','why','will','would','yet','you','your','They','Look','Good','A', 'Able','About','Across','After','All','Almost','Also','Am','Among','An','And','Any','Are','As','At','Be','Because','Been','By','Did','Else','Ever','Every','For','From','Get','Got','Had','Has','Have','He','Her','Hers','Him','His','How','However','I','If','In','Into','Is','It','Its','Just','Least','Let','May','Me','Might','My','Of','Off','On','Or','Other','Our','Own','Rather','Said','Say','Says','She','Should','Since','So','Than','That','The','Their','Them','Then','There','These','They','This','Tis','To','Was','Us','Was','We','Were','What','When','Where','While','Who','Whom','Why','Will','Would','Yet','You','Your','!','@','#','"','$','(','.',')']
clean = usertext.apply(lambda x: [word for word in x if word not in stop_words])
df['clean']=clean
print(df['clean'])
print("\n")
print("Pos_Tagging..............\n\n")
df['tagged_texts'] = df.apply(lambda df:nltk.pos_tag(df['clean']),axis=1)
print(df['tagged_texts'])
print("\n\n")
print("Array..............\n\n")
tagged=np.array(df['tagged_texts'])
print(tagged)
pos=neg=obj=count=0
for word, tag in tagged:
ss_set = None
if 'NN' in tag and swn.senti_synsets(word):
ss_set = list(swn.senti_synsets(word))[0]
elif 'VB' in tag and swn.senti_synsets(word):
ss_set = list(swn.senti_synsets(word))[0]
elif 'JJ' in tag and swn.senti_synsets(word):
ss_set = list(swn.senti_synsets(word))[0]
elif 'RB' in tag and swn.senti_synsets(word):
ss_set = list(swn.senti_synsets(word))[0]
if ss_set:
pos=pos+synset.pos_score()
neg=neg+synset.neg_score()
obj=obj+synset.obj_score()
count+=1
final_score=pos-neg
print(final_score)
df['final_score']=final_score
df.to_csv('smallcsvdata2.csv')
norm_finalscore= round((final_score) / count, 2)
print(norm_finalscore)
final_sentiment = 'positive' if norm_finalscore >= 0 else 'negative'
print(final_sentiment)
Results
Traceback (most recent call last):
File "C:\Users\Sheikh Aijaz\Desktop\O_M\sentiwordnet\o_m.py", line 33, in
<module>
for word, tag in tagged_texts:
ValueError: too many values to unpack (expected 2)
