A Comparative Study of Machine Learning and Lexicon-Based Methods for Sentiment Classification

Srinivasan, Ammaiappan (2025) A Comparative Study of Machine Learning and Lexicon-Based Methods for Sentiment Classification. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study is a comparison between machine learning and lexicon-based sentiment classification on Twitter data using Sentiment140 dataset of 1.6 million tweets. The paper has constructed an entire sentiment analysis pipeline including preprocessing, feature extraction, training of the model and evaluation. TF-IDF vectorization was done to clean, tokenize and lemmatize the text data which was then classified using four supervised models which included Naive Bayes, Logistic Regression, Support Vector Machine (SVM) and random forest. Accuracy, precision, recall and F1-score were used to measure the performance. Findings indicated that both Logistic Regression and Linear SVM scored the highest accuracy and F1-scores of about 78 and better than lexicon-based methods on contextual adaptability and predictive reliability. Even though lexicon-based methods were interpretable and cheap to compute, they could not handle informal and ambiguous language. The paper finds that linear machine learning fits offer intermediate between scalability and accuracy and that the next research should adopt hybrid or transformer-based systems to capture the contextual sentiment in greater detail.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Agarwal, Bharat UNSPECIFIED
Uncontrolled Keywords:	Sentiment Analysis; Twitter Data; Machine Learning; Lexicon-Based Methods; Natural Language Processing; TF-IDF; Logistic Regression; Support Vector Machine; Text Classification; Opinion Mining
Subjects:	P Language and Literature > P Philology. Linguistics > Computational linguistics. Natural language processing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	09 Sep 2026 10:02
Last Modified:	09 Sep 2026 10:02
URI:	https://norma.ncirl.ie/id/eprint/9917

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