add text analyze and text analyze using NLTK

textAnalyze(NLTK).py

+import string
+from collections import Counter
+
+# import matplotlib.pyplot as plt
+from nltk.corpus import stopwords
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+from nltk.stem import WordNetLemmatizer
+from nltk.tokenize import word_tokenize
+
+text = open('read.txt', encoding='utf-8').read()
+lower_case = text.lower()
+cleaned_text = lower_case.translate(str.maketrans('', '', string.punctuation))
+
+# Using word_tokenize because it's faster than split()
+tokenized_words = word_tokenize(cleaned_text, "english")
+
+# Removing Stop Words
+final_words = []
+for word in tokenized_words:
+    if word not in stopwords.words('english'):
+        final_words.append(word)
+
+# Lemmatization - From plural to single + Base form of a word (example better-> good)
+lemma_words = []
+for word in final_words:
+    word = WordNetLemmatizer().lemmatize(word)
+    lemma_words.append(word)
+
+emotion_list = []
+with open('emotions.txt', 'r') as file:
+    for line in file:
+        clear_line = line.replace("\n", '').replace(",", '').replace("'", '').strip()
+        word, emotion = clear_line.split(':')
+
+        if word in lemma_words:
+            emotion_list.append(emotion)
+
+print(emotion_list)
+w = Counter(emotion_list)
+print(w)
+
+
+def sentiment_analyse(sentiment_text):
+    score = SentimentIntensityAnalyzer().polarity_scores(sentiment_text)
+    if score['neg'] > score['pos']:
+        print("Negative Sentiment")
+    elif score['neg'] < score['pos']:
+        print("Positive Sentiment")
+    else:
+        print("Neutral Sentiment")
+
+
+sentiment_analyse(cleaned_text)
+
+

textAnalyze.py

+import string
+from collections import Counter
+
+# import matplotlib.pyplot as plt
+
+# reading text file
+text = open("read.txt", encoding="utf-8").read()
+
+# converting to lowercase
+lower_case = text.lower()
+
+# Removing punctuations
+cleaned_text = lower_case.translate(str.maketrans('', '', string.punctuation))
+
+# splitting text into words
+tokenized_words = cleaned_text.split()
+
+stop_words = ["i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself",
+              "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself",
+              "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these",
+              "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do",
+              "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while",
+              "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before",
+              "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again",
+              "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each",
+              "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than",
+              "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"]
+
+# Removing stop words from the tokenized words list
+final_words = []
+for word in tokenized_words:
+    if word not in stop_words:
+        final_words.append(word)
+
+
+emotion_list = []
+with open('emotions.txt', 'r') as file:
+    for line in file:
+        clear_line = line.replace("\n", '').replace(",", '').replace("'", '').strip()
+        word, emotion = clear_line.split(':')
+
+        if word in final_words:
+            emotion_list.append(emotion)
+
+print(emotion_list)
+w = Counter(emotion_list)
+print(w)
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