Social Media Sentiment Analysis: Twitter Dataset

Aarya Dalvi, Mahek Dharod, Manisha Tiwari

2025

Abstract

Sentiment analysis is an important area in natural language processing (NLP), which helps in extracting meaningful insights from text-based data. This paper explores the application of sentiment analysis techniques, with a particular focus on the Complement Naive Bayes (CNB) model, to assess sentiment polarity in user-generated content. The research aims to evaluate how effectively the CNB model classifies text as either positive or negative, thus contributing to a more comprehensive understanding of methods in sentiment analysis. This study utilizes a dataset of tweets, a widely used form of user-generated content, as the basis for its analysis. Preprocessing steps such as tokenization, lemmatization, and text cleaning are conducted to prepare the data for feature extraction, which is done using the CountVectorizer method. The Complement Naive Bayes (CNB) model was chosen due to its effectiveness in handling imbalanced datasets and its improvements over the traditional Naive Bayes algorithm. Through various tests and evaluations, the study demonstrates that CNB can accurately classify sentiment. Metrics like accuracy, precision, recall, and F1 score provide quantitative insights into the model's performance, while the Receiver Operating Characteristic (ROC) curve offers a visual representation of its discriminative power.

Paper Citation

in Harvard Style

Dalvi A., Dharod M. and Tiwari M. (2025). Social Media Sentiment Analysis: Twitter Dataset. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 529-537. DOI: 10.5220/0013623700004664

in Bibtex Style

@conference{incoft25,
author={Aarya Dalvi and Mahek Dharod and Manisha Tiwari},
title={Social Media Sentiment Analysis: Twitter Dataset},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT},
year={2025},
pages={529-537},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013623700004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 3: INCOFT
TI - Social Media Sentiment Analysis: Twitter Dataset
SN - 978-989-758-763-4
AU - Dalvi A.
AU - Dharod M.
AU - Tiwari M.
PY - 2025
SP - 529
EP - 537
DO - 10.5220/0013623700004664
PB - SciTePress