Emotion Detection using Social Media and Machine Learning

Abstract

Today we do a lot of things online from shopping to data sharing on social networking sites. Social networking (SNS) is good for releasing stress and depression by sharing one’s thoughts. Thus, emotion detection has become a hot trend to day. But there is a problem in analyzing emotions on a SNS like twitter as it generates lakhs of tweets each day and it is hard to keep track of the emotion behind each tweet as it is impossible for a human being to read and decide the emotions behind tweets. So, to help understand behind the texts in a SNS site we thought of designing a project which will keep track of the tweets and predict the right emotion behind the tweets whether they have a positive or a negative sentiment behind them. This thought of project can be achieved by a integration of SNS with NLP and machine learning together. For SNS we will use Twitter as it generates a lot of data which is accessible freely using an API. First, we will enter a keyword and fetch tweets from the twitter. Then stop words will be removed from these tweets using NLTK stop words database. Then the tweets will be passed for POS tagging and only right form of grammatical words will be kept and others will be removed. Then we create a training dataset with two types positive and negative. Then SVM algorithm will be trained using this training dataset. Then each tweet will be passed to the SVM as testing dataset which in turn will return classification of each tweet as a whole in two classes positive and negative. Thus, our application will be helpful in recognizing emotion behind a tweet.