Sentiment Analysis of Twitter Data Using TF-IDF and Machine Learning Techniques

• DOI: 10.1109/com-it-con54601.2022.9850477
• Publication Date: 2022-08-15T20:04:59Z
• Authors: Satyendra Singh; Krishan Kumar; Brajesh Kumar
• Keywords: Sentiment Analysis; tf–idf

Sentiment analysis technique plays an important role in natural language processing to analyze complex human statements. In the last few years, this has become a powerful tool for several social media communication mediums such as WhatsApp, Twitter, Facebook, Instagram, YouTube, LinkedIn, Blog, etc. This paper proposes machine learning (ML) based method data sentiment on text data. The presented is divided into three distinct stages. first stage, pre-processing performed filter and refine second feature extraction using Term Frequency Inverse Document (TF-IDF) technique. Moreover, during third extracted features are supplied make predictions classifier. experiments carried out publicly available Twitter dataset US Airlines. Several ML techniques utilized classification. results reported different evaluation metrics like accuracy, precision, recall, F1 score. Finally, support vector yielded most relevant results.