sentimental analysis

analysis.py

+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+#analyzer = SentimentIntensityAnalyzer()
+
+def overall_sentiment(text):
+    v = SentimentIntensityAnalyzer()
+    percent_sentiment = v.polarity_scores(text)
+    print("Text is:", text)
+    print("Breakdown: ", percent_sentiment)
+    print("Text was rated as ", percent_sentiment['neg']*100, "% Negative")
+    print("Text was rated as ", percent_sentiment['neu']*100, "% Neutral")
+    print("Text was rated as ", percent_sentiment['pos']*100, "% Positive")
+
+    print("Sentence overall rated As", end = " ")
+ 
+    if percent_sentiment['neg'] < 0.1:
+        if percent_sentiment['pos']-percent_sentiment['neg'] > 0 or percent_sentiment['compound'] >= 0.5:
+            print("Positive")
+            return "Positive"
+ 
+    elif percent_sentiment['pos'] < 0.1:
+        if percent_sentiment['pos']-percent_sentiment['neg'] <= 0 or percent_sentiment['compound'] <= -0.5: # The "=" makes it better
+            print("Negative")
+            return "Negative"
+        
+    else :
+        print("Neutral")
+        return "Neutral"
+
+
+pos_count = 0
+pos_correct = 0
+
+file_positive = open("positive.txt","r")
+file_positive_lines = file_positive.readlines()
+for line in file_positive_lines:
+    sentiment = overall_sentiment(line)
+    if sentiment == "Positive":
+            pos_correct += 1
+    pos_count += 1
+
+neg_count = 0
+neg_correct = 0
+
+file_negative = open("negative.txt","r")
+file_negative_lines = file_negative.readlines()
+for line in file_negative_lines:
+    sentiment = overall_sentiment(line)
+    if sentiment == "Negative":
+        neg_correct += 1
+    neg_count += 1
+
+
+print("Positive accuracy using vader= {}% via {} samples".format(pos_correct/pos_count*100.0, pos_count))
+print("Negative accuracy using vader= {}% via {} samples".format(neg_correct/neg_count*100.0, neg_count))
+print(" ")
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analysis_experiment.py

+from typing import Text
+from textblob import TextBlob
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
+
+# textblob gives polarity(sentiment ranging from -1 to 1) and subjectivity (ranging 0 to 1) values. 
+# text1 = "The food at Bankok Thai is good"
+# text2 = "Thanksgiving is comming soon"
+# text3 = "The ball is blue"
+
+# complex_text = "This does not taste good" # inaccurate when not is used
+# blob1 = TextBlob(text1)
+# blob2 = TextBlob(text2)
+# blob3 = TextBlob(text3)
+
+# blob_complex_text = TextBlob(complex_text)
+# print(blob1.sentiment)
+# print(blob2.sentiment)
+# print(blob3.sentiment)
+# print(blob_complex_text.sentiment)
+
+
+pos_count = 0
+pos_correct = 0
+
+file_positive = open("positive.txt","r")
+file_positive_lines = file_positive.readlines()
+for line in file_positive_lines:
+    analysis = TextBlob(line)
+
+    # if analysis.sentiment.subjectivity >= 0.0001: # find optimal threshold
+    if analysis.sentiment.polarity > 0: # find optimal threshold
+        pos_correct += 1
+    pos_count +=1
+
+
+neg_count = 0
+neg_correct = 0
+
+file_negative = open("negative.txt","r")
+file_negative_lines = file_negative.readlines()
+for line in file_negative_lines:
+    analysis = TextBlob(line)
+
+        #if analysis.sentiment.subjectivity > 0.0001: # find optimal threshold
+    if analysis.sentiment.polarity <= 0: # find optimal threshold
+        neg_correct += 1
+    neg_count +=1
+
+print("Positive accuracy using textBlob= {}% via {} samples".format(pos_correct/pos_count*100.0, pos_count))
+print("Negative accuracy using textBlob= {}% via {} samples".format(neg_correct/neg_count*100.0, neg_count))
+
+print("Using vaderSentiment")
+analyzer = SentimentIntensityAnalyzer()
+
+# The Compound score is a metric that calculates the sum of all the lexicon ratings which have been normalized between -1(most extreme negative) and +1 (most extreme positive).
+# positive sentiment : (compound score >= 0.05) 
+# neutral sentiment : (compound score > -0.05) and (compound score < 0.05) 
+# negative sentiment : (compound score <= -0.05)
+
+pos_count = 0
+pos_correct = 0
+
+file_positive = open("positive.txt","r")
+file_positive_lines = file_positive.readlines()
+for line in file_positive_lines:
+    v = analyzer.polarity_scores(line)
+    if v['neg'] < 0.1:
+        if v['pos']-v['neg'] > 0 or v['compound'] >= 0.5:
+            pos_correct += 1
+    pos_count += 1
+
+neg_count = 0
+neg_correct = 0
+
+file_negative = open("negative.txt","r")
+file_negative_lines = file_negative.readlines()
+for line in file_negative_lines:
+    v = analyzer.polarity_scores(line)
+    if v['pos'] < 0.1:
+        if v['pos']-v['neg'] <= 0 or v['compound'] <= -0.5: # The "=" makes it better
+            neg_correct += 1
+    neg_count += 1
+
+
+print("Positive accuracy using vader= {}% via {} samples".format(pos_correct/pos_count*100.0, pos_count))
+print("Negative accuracy using vader= {}% via {} samples".format(neg_correct/neg_count*100.0, neg_count))
+print(" ")
+
+
+
+def overall_sentiment(text):
+    v = SentimentIntensityAnalyzer()
+    percent_sentiment = v.polarity_scores(text)
+    print("Text is:", text)
+    print("Breakdown: ", percent_sentiment)
+    print("Text was rated as ", percent_sentiment['neg']*100, "% Negative")
+    print("Text was rated as ", percent_sentiment['neu']*100, "% Neutral")
+    print("Text was rated as ", percent_sentiment['pos']*100, "% Positive")
+
+    print("Sentence overall rated As", end = " ")
+ 
+    if percent_sentiment['neg'] < 0.1:
+        if percent_sentiment['pos']-percent_sentiment['neg'] > 0 or percent_sentiment['compound'] >= 0.5:
+            print("Positive")
+ 
+    elif percent_sentiment['pos'] < 0.1:
+        if percent_sentiment['pos']-percent_sentiment['neg'] <= 0 or percent_sentiment['compound'] <= -0.5: # The "=" makes it better
+            print("Negative")
+        
+    else :
+        print("Neutral")
+    print(" ")
+
+overall_sentiment("Yesterday was the worst day of my life.")
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negative.txt

+I cannot learn a new language.
+I cannot stand him.
+I am afraid I might fail.
+People do not like me.
+I never succeed at anything.
+I don’t believe I will get the job.
+I am not as good as him/her.
+I can’t remember the next line! Ah, I don’t know if I can memorize this poem by the end of the week.
+Before he makes any purchases, Billy likes to do his research; he’s so cheap.
+The more you work with him, the more you’ll find he’s a very stubborn man.
+He’s too lazy to clean the dust off the ceiling fan.
+Our new boss is incredibly pushy.
+She’s a very nosy child.
+Let’s just say she’s dumb.
+At work and at home, she’s very egotistical.
+THe food is too salty. Get rid of it immediately.
+Lots of people are dying from COVID.
+Yesterday was the worst day of my life.
+I'm going to fail some of exams this upcoming week.
+I have no idea what's going on in this class.
+I got fired from work today.
+I have cancer.
+I lost $400 from poker and blackjack last month in Nevada.
+The process of manually generating sentences is very tedious. 
+The food is too expensive. Let's forget about this place.
+My car broke down a week ago. Now I am going to be overwhelmed this upcoming week.
+Becky was rude to Chad.
+His high ego let to his own downfall.
+I'm going to lose in this upcomming swim meet.
+The fridge has rotten food and cockroaches in some containers. 
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positive.txt

+I can get this job and do it well.
+Today is a wonderful day, when everything moves smoothly and harmoniously.
+I always try to be optimistic and hope for the best.
+I expect to get good grades.
+I am doing my best to improve my habits and myself.
+We expect everything to turn out well.
+Today is going to be a wonderful day and everything is going to move smoothly and harmoniously.
+I am full of energy.
+I am sure there is good in this situation.
+I trust myself to make the best decision.
+I am enjoying the work that I am doing.
+My dog always hopes for the best and expects the best.
+I let go all my anger and frustrations and replace them with peace and hopes.
+I can earn the money to provide a comfortable life for my family and for myself.
+People love and respect me, and enjoy my company.
+He always try to keep on open mind, without criticism.
+Ben chooses to find hopeful and optimistic ways to look at the situation.
+She feels confident and at ease in the company of any person.
+I always find reasons to be happy and optimistic.
+I have very good relations with my family, friends and coworkers.
+Jane forgives people and treats them with respect.
+I can achieve all my ambitions.
+I can solve every problem and handle well every situation.
+I can finish my work on time.
+Everything in my life is improving and getting better.
+You’re an amazing mother!
+You have the best voice. I love hearing you sing!
+You can do it, Chad!
+Don’t give up!
+This piano piece sounds excellent; you must have practiced a lot. Keep it up!
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