Twitter-sentiment-analysis

Twitter is an online microblogging and social networking platform, which allows users to write short status, updates of maximum length 280 characters. These tweets reflect public sentiment about various topics and events happening. Analysing the public sentiment can help, firms trying to find out the response of their products in the market, predicting political elections and predicting socioeconomic phenomena like stock exchange. Sentiment analysis techniques are widely popular for this purpose. In this paper, we have tried to define and compare various sentiment classification approaches/methods for finding out the sentiments behind the tweet. Sentiment analysis is a progressive field of natural language processing. It is a way to detect the attitude, state of mind, or emotions of the person towards a product, service, movie, etc. by analyzing the opinions and reviews shared on social media, blogs and so on. Various social media platforms such as Facebook, Twitter and so on allow people to share their views with other people. Twitter become the most popular social media platform that allows users to share information by way of the short messages called tweets on a real-time basis. Thousands of people interact with each other at the same time and a huge amount of data is produced in seconds. To make good use of this data, we develop a Real-time twitter sentiment analysis and visualization system called TwiSent. It is a web application and its purpose is to employ an open source approach for sentiment analysis and its visualization using a set of packages supported by python language to mine the real-time data from Twitter through application programming interfaces (APIs) using hashtags and keywords. This system will analyze the sentiments as positive and negative for a particular product and service that helps organizations, political parties, and common people to understand the effectiveness of their efforts and better decision making. Our experimental results show that TwiSent can process data in real-time, and obtain visualize information continuously.