Twitter Sentiment Analysing Using AI

Sentiment analysis is a technique used to gather viewpoints, attitudes, and feelings from social media platforms like Twitter. It is now a well-liked field of study. Textual data is the main emphasis of the traditional approach to sentiment a Twitter is the most popular microblogging social networking site where users can send updates in the form of tweets on various topics. In this work, a labeled dataset that is freely accessible on Kaggle is employed, and a thorough set of pre-processing procedures is organized to gradually make the tweets suitable for handling using standard language handling techniques. As every instance in the collection consists of two tweets and a sentiment. Hence, machine learning under supervision is employed. Artificial intelligence classifiers can be used for this. These classifiers assess opinions on certain entities by endorsing political parties, businesses, analysts, etc. Machine learning methods for data appropriately classify the tweets through training. This means that machine learning techniques are more effective and quicker at performing sentiment analysis because this method does not require a word database. KEYWORDS: Supervised machine learning, Sentiment analysis ,Twitter,Data mining, Product evaluation ,ROC ,Classification ,Naive Bayes Logistic regression, Support vector machine and Linear SVC