Comparison Analysis of Logical Regression and Random Forest with Word Embedding Techniques for Twitter Sentiment Analysis
Dhiraj Singh · BENTHAM SCIENCE PUBLISHERS eBooks · 2025
In order to carry out categorization and generate new categories, the huge volumes of textual materials produced nowadays must be immediately organised. The fundamental method of gaining insights from organising textual data is text classification. Then, we further classify the classes based on the discovered text types. Separated into four stages—pre-treatment, text representation, classifier execution, and classification—we use a wide range of machine learning approaches to classify texts. In this study, we utilise real-world data from Twitter to evaluate and compare several sentiment analysis approaches. We clean the data and divide it into train and text set before developing models using various vectorising approaches and compare the outcomes. Based on a comparison of the models with various vectorizations, it was found that the best performance was provided by the Logical Regression (LR) models using TF-IDF, with an f1 value of 0.81 and good accuracy and recollection values.