Twitter Sentiment Analysis using Naive Bayes Algorithm

Sentiment analysis mines social media networks like Twitter for views, attitudes, and sentiments. It is presently a popular study subject. The traditional sentiment analysis approach emphasises textual data the most. Twitter is the most well-known microblogging social networking service, where users transmit updates on numerous topics as tweets. This research uses a publicly available labelled dataset on Kaggle. A comprehensive series of data cleaning and preparation techniques are organised to progressively make the tweets more comprehensible to typical language handling algorithms. Because each dataset sample consists of a pair of tweets and a sentiment, the Supervised machine learning concept was used in this work. Additionally, Nave Bayes-based sentiment analysis model is presented. The primary purpose is to discover tweet analysis more efficiently. Twitter sentiment analysis divides tweets into positive and negative attitudes. Using training data, machine learning algorithms correctly categorise the tweets. As a result, this strategy eliminates the requirement for a word database, making machine-learning approaches more effective and faster for sentiment analysis.