Analysis.py

Author: ARUNAGIRINATHAN-K

Facebook Sentiment Analysis using python/Analysis.py

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+import time
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import nltk
+import io
+import unicodedata
+import numpy as np
+import re
+import string
+from numpy import linalg
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+from nltk.tokenize import sent_tokenize, word_tokenize
+from nltk.tokenize import PunktSentenceTokenizer
+from nltk.tokenize import PunktSentenceTokenizer
+from nltk.corpus import webtext
+from nltk.stem.porter import PorterStemmer
+from nltk.stem.wordnet import WordNetLemmatizer
+
+
+with open('kindle.txt', encoding ='ISO-8859-2') as f:
+    text = f.read()
+    
+sent_tokenizer = PunktSentenceTokenizer(text)
+sents = sent_tokenizer.tokenize(text)
+
+print(word_tokenize(text))
+print(sent_tokenize(text))
+
+porter_stemmer = PorterStemmer()
+
+nltk_tokens = nltk.word_tokenize(text)
+
+for w in nltk_tokens:
+    print ("Actual: % s Stem: % s" % (w, porter_stemmer.stem(w)))
+    
+
+wordnet_lemmatizer = WordNetLemmatizer()
+nltk_tokens = nltk.word_tokenize(text)
+
+for w in nltk_tokens:
+    print ("Actual: % s Lemma: % s" % (w, wordnet_lemmatizer.lemmatize(w)))
+    
+text = nltk.word_tokenize(text)
+print(nltk.pos_tag(text))
+
+sid = SentimentIntensityAnalyzer() 
+tokenizer = nltk.data.load('tokenizers / punkt / english.pickle')
+
+with open('kindle.txt', encoding ='ISO-8859-2') as f:
+    for text in f.read().split('\n'):
+        print(text)
+        scores = sid.polarity_scores(text)
+        for key in sorted(scores):
+            print('{0}: {1}, '.format(key, scores[key]), end ='')
+             
+    print()