Lexicon-Based Sentiment Analysis on Text Polarities with Evaluation of Classification Models

TL;DR

This paper evaluates lexicon-based sentiment analysis methods and compares various classification models on a large Twitter dataset, highlighting Random Forest's superior performance in classifying text polarity.

Contribution

It introduces a comprehensive evaluation of lexicon-based sentiment analysis combined with machine learning models on Twitter data, including a novel neutrality measure and personality judgment application.

Findings

Random Forest achieved 81% accuracy in sentiment classification.

Lexicon-based methods effectively identify emotion intensity and polarity.

Support Vector Machines and Naive Bayes performed competitively in the analysis.

Abstract

Sentiment analysis possesses the potential of diverse applicability on digital platforms. Sentiment analysis extracts the polarity to understand the intensity and subjectivity in the text. This work uses a lexicon-based method to perform sentiment analysis and shows an evaluation of classification models trained over textual data. The lexicon-based methods identify the intensity of emotion and subjectivity at word levels. The categorization identifies the informative words inside a text and specifies the quantitative ranking of the polarity of words. This work is based on a multi-class problem of text being labeled as positive, negative, or neutral. Twitter sentiment dataset containing 1.6 million unprocessed tweets is used with lexicon-based methods like Text Blob and Vader Sentiment to introduce the neutrality measure on text. The analysis of lexicons shows how the word count and the intensity classify the text. A comparative analysis of machine learning models, Naiive Bayes, Support Vector Machines, Multinomial Logistic Regression, Random Forest, and Extreme Gradient (XG) Boost performed across multiple performance metrics. The best estimations are achieved through Random Forest with an accuracy score of 81%. Additionally, sentiment analysis is applied for a personality judgment case against a Twitter profile based on online activity.