Twitter Sentiment Analysis

Mert Kahyaoğlu

Instructor: Assoc. Prof. Dr. Bekir Taner Dinçer

NLP Course Project

AIM OF THE PROJECT

The aim of the project is to determine how people are feeling when they share something on twitter.

For example; 

“ Final projeleri çok zevkli! ”      =>    Positive

" Finaller çok kötü geçiyor :( ”   =>    Negative

ROAD MAP

1.Collect Tweets

• Tweets;

• from Twitter API

• using Tweepy Helper Library

• Requirements

• Twitter Developer Application for authantication

2.Clean Tweets

• Hashtags ( # )

• Remove;

• Mentions ( @ )

• Links ( http://.. )

• Retweets ( RT .. )

• Limit ( length(word) > 2 )

• Lowercase

3. LABEL & ForMAT

• Emoticons ( 😃 )

• Label by;

• Adjectives

• Textual Emoticons ( ":D" )

• Format for Weka (.arff);

4.EXTRACT FEATURES

1. Apply Weka -> StringToWordVector Algorithm

"finaller çok kötü geçiyor"  -->  ["finaller", "çok", "kötü", "geçiyor"]

2. Attribute Selection

Selects attributes considering the individual predictive ability of each feature along with the degree of redundancy between them.

• Apply Weka ->  CfsSubsetEval Algorithm

• Save new set of attributes as .arff file

Reduced terms from 930 to 24

5.CLASSIFY

• Boost Naive Bayes with AdaBoostM1 Algorithm

• What is Boosting?

• Train models (Naive Bayes) in a sequence

• Weights models according to performance

• New models are influenced by performance of previously built ones

• Encourage new model to focus on those cases which were incorrectly classified in the last round

• Combine the classifiers by letting them vote on the final prediction 

USAGE

Results

Web APPLICATION

• Shows positive and negative tweets of your Twitter accounts

• https://twitter-sentiment-analysis.now.sh/

thank you for lıstenıng