Year: 2024
Type: article
Abstract: Social media platforms like Twitter have become a rich source of real-time data and public sentiment. Analysing sentiment on Twitter is essential for various applications, from brand monitoring to political analysis. This work focuses on Twitter sentiment analysis, employing natural language processing (NLP) techniques to categorize tweets as positive, negative, or neutral. We collect a large dataset of tweets, pre-process the text, and train machine learning models to predict sentiment. Our goal is to provide insights into public sentiment on various topics, trends, and events, which can be valuable for decision-makers in diverse domains.
Authors: Soniya Zore, Amol Bhosale, Pratibha Chavan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4408008827