With more than 321 million active users, sending a daily average of 500 million Tweets, Twitter allows businesses to reach a broad audience and connect with customers without intermediaries. On the downside, it’s harder for the users to know whether the tweets are positive, neutral or negative. With Twitter sentiment analysis you can get the analysis of a Tweet in real-time and gain valuable insights on that tweet. Social media have received more attention nowadays. Public and private opinion about a wide variety of subjects are expressed and spread continually via numerous social media. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyse customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. This project reports on the design of a sentiment analysis. The approach adopted is to analyse the lexicon features of the tweets for classifying its sentiment (positive, negative and neutral). The training data is made more exhaustive by including various manually labelled tweets, in addition to the existing word stock to keep up with the changing microblogging trends. After this initial preprocessing, the machine learning algorithms are Support vector machines, Random Forest, Naïve Bayes and XGBoost Algorithms will be applied and accuracy will be measured.
1. Support Vector Machine
2. Random Forest
3. Naive Bayes
- Twitter Sentiment Analysis Reference Paper 00:00:00
- Twitter Sentiment Analysis Synopsis 00:00:00