TWITTER SENTIMENT ANALYSIS
Jayant Mishra
MCA (Data Science & Artificial Intelligence), Babu Banarasi Das University, Lucknow, Uttar Pradesh, India- jrnarayan@gmail.com
Abhishek Kumar
MCA (Data Science & Artificial Intelligence), Babu Banarasi Das University, Lucknow, Uttar Pradesh, India- abhishek221197@gmail.com
Dr. Prabhash Chandra Pathak
Head of Department, School of Computer Application, Babu Banarasi Das University, Lucknow, Uttar Pradesh, India- Hod.ca@bbdu.ac.in
Abstract
Sentiment analysis is a popular research field that aims to extract opinions, attitudes, and emotions from social media platforms like Twitter. Traditionally, sentiment analysis has focused on analyzing textual data. Twitter, being a well-known microblogging platform where users post updates in the form of tweets, is often used as a data source for sentiment analysis studies.
In this particular paper, the authors utilize a publicly available labeled dataset from Kaggle. They also propose a set of preprocessing steps to enhance the manageability of tweets for natural language processing techniques. Since the dataset consists of pairs of tweets and their corresponding sentiment labels, supervised machine learning is employed. The authors propose sentiment analysis models based on naive Bayes, logistic regression, and support vector machine algorithms with the objective of achieving more effective sentiment analysis. In Twitter sentiment analysis, tweets are typically categorized into positive or negative sentiment. Machine learning classifiers can be utilized for this purpose. These classifiers can be valuable for various entities such as businesses, political parties, and analysts, as they can evaluate the sentiments expressed towards them. By appropriately training the machine learning models with labeled data, tweets can be accurately classified without the need for a predefined word database. Consequently, machine learning techniques offer a superior and faster approach to performing sentiment analysis.
Keywords: Twitter; sentiment; Web data; text mining; SVM; Bayesian algorithm; hybrid; ensembles
