Sentiment Analysis Using NLP: An Objective and Expectation-Based Approach

Abstract

This research paper presents a comparative analysis of machine learning (ML) algorithms applied to Twitter sentiment analysis, focusing on two distinct text representation approaches: Bag of Words and TF-IDF. The study employs a diverse set of ML models, including Logistic Regression, XG Boost, Decision Tree, Naive Bayes, Random Forest, and Support Vector. The utilization of Bag of Words and TF-IDF as text representation techniques adds depth to the analysis. Bag of Words represents text data by counting the frequency of words in a document, while TF-IDF (Term Frequency-Inverse Document Frequency) weighs the importance of words based on how frequently they appear in a document relative to their frequency across the entire corpus. Comparing the performance of ML models using these two approaches offers insights into which method may be more suitable for sentiment analysis tasks on Twitter data. Overall, the research paper delves into the nuances of ML algorithms and text representation techniques in the context of sentiment analysis, with a focus on their application to Twitter data. The findings of this study could contribute to advancements in sentiment analysis methodologies and aid in better understanding the sentiment dynamics of social media platforms like Twitter.