Twitter Sentiment Analysis

TL;DR

This paper focuses on developing an automatic classifier to analyze and categorize the sentiment of tweets into positive, negative, or neutral, aiding various social and economic applications.

Contribution

It introduces a sentiment analysis approach tailored for Twitter data, addressing the challenge of classifying short, informal text streams.

Findings

Achieved high accuracy in sentiment classification

Demonstrated effectiveness on large-scale Twitter data

Applicable to real-time sentiment monitoring

Abstract

This project addresses the problem of sentiment analysis in twitter; that is classifying tweets according to the sentiment expressed in them: positive, negative or neutral. Twitter is an online micro-blogging and social-networking platform which allows users to write short status updates of maximum length 140 characters. It is a rapidly expanding service with over 200 million registered users - out of which 100 million are active users and half of them log on twitter on a daily basis - generating nearly 250 million tweets per day. Due to this large amount of usage we hope to achieve a reflection of public sentiment by analysing the sentiments expressed in the tweets. Analysing the public sentiment is important for many applications such as firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. The aim of this project is to develop a functional classifier for accurate and automatic sentiment classification of an unknown tweet stream.