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

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