This document describes a sentiment analysis tool developed by Ravindra Chaudhary and Sachin Singh under the guidance of Mrs. Smita Tiwari. It uses a Naive Bayes classifier to analyze tweets and classify the sentiment as positive, negative, or neutral. The methodology involves collecting tweets using the Twitter API, preprocessing the text by removing URLs, hashtags, numbers, and other unnecessary words. Features are then extracted such as capitalized words and emoticons. The preprocessed text and features are fed into the Naive Bayes classifier to predict the sentiment. The tool was implemented using technologies like NET BEANS IDE, WAMP SERVER, MYSQL, HTML5 and CSS. Future work could involve converting it to a

1. SENTIMENTAL ANALYSIS
TOOL
BY:-
RAVINDRA CHAUDHARY
SACHIN SINGH
UNDER THE GUIDENCE OF
MRS. SMITA TIWARI

2. CONTENT
• Introduction
• Problem Statement
• Objective
• Tools/Techniques
• Methodology
• Implementation
• Results & Discussion
• Conclusion
• Future Scope of the project

3. INTRODUCTION
What is Sentiment Analysis…??
It is the classification of the polarity of given text in the document.
The goal is to determine whether the expressed opinion in the text is
Positive , Negative or Neutral.
For Example:-
Positive :- sarvjeet is good guy…
negative :- jasleen is misusing the law..
Neutral :- waiting for court decision..

4. Why using twitter for sentiment analysis:-
• Social networking and microblogging website.
• Short text messages 140 Character.
• 316+ million active users and 500 million tweets per day generated
• People share their thoughts using twitter it may be any social issue
,movie ,politics , news and so on.
• Also share current affairs and personal view on different topics..
• The challenge is to gather all such relevant data , detect and
summarize the overall sentiment on a topic.

5. Problem Statement
• The problem in the sentiment analysis is classifying the polarity of
given text in a document in a sentence
• Whether the expressed opinion in the document or in a sentence is
positive ,negative or neutral.

6. Objective
• To implement an Algorithm(Naïve Bayes algorithm) for classification
to text into Positive , Negative ,or Neutral.
• Making more data set for more accurate results.

7. Naïve Bayes Classifiers
• In machine learning, naive Bayes classifiers are a family of simple
probabilistic classifiers based on applying Bayes' theorem with strong
(naive) independence assumptions between the features
• Naive Bayes has been studied extensively since the 1950s. It was
introduced under a different name into the text retrieval
• community in the early 1960s, and remains a popular (baseline)
method for text categorization,

8. • the problem of judging documents as belonging to one category or
the other (such as spam or legitimate, sports or politics, etc.) with
word frequencies as the features
• Naive Bayes classifiers are highly scalable, requiring a number of
parameters linear in the number of variables

10. NAÏVE BAYES EXAMPLE:-

17. Tools/Techniques
• NET BEANS IDE 8.0
• WAMP SERVER
• MY SQL
• HTML5
• CSS
• JAVA

18. Methodology

19. Methodology
1. DATA COLLECTION
• download the tweets using Twitter 4J API.
2. TOKENSIER
• Twitter using POS(part of speech) tagger.
.

20. 3. PRE-PROCESSING
• Remove slag words.
• Remove URL and HASTAG(#),numbers.
• Replace sequence of repeated character coooooool by cool.
• Remove noun and prepositions

21. FEATURE EXTRACTION
• Percentage of capitalized word
• No of –ve /+ve capitalized word
• No of +ve /-ve hashtag
• No of +ve /-ve emoticons
• No. of negations
• No. of special characters ex..@#%^*
CLASSIFICATION AND PREDECTIONS
• The model is built to predict the sentiment of new tweets…
• Feature extracted are next focused to classifier

22. HOME page

28. Types of Classification
1. Binary classification:-
only Positive , Negative .
2. 3 Teir:-
Positive , Negative and Neutral .
3. 5 Teir :- :-
 Extremely Positive , Extremely Negative , Positive , Negative
and Neutral

29. Future scope
• Web application can be converted to mobile applications
• Sentiment analysis may be implemented in future for accuracy
purposes
• Updating dictionary for new synonyms and antonyms

30. Conclusion
By improving the data sets we get more accurate results
(sentiments).

31. THANKYOU EVERYONE