Exploring text classification through the lens of Artificial Intelligence. A Comparative Analysis and Evaluation
Year: 2023
Type: dissertation
Abstract: This master's thesis aims to contribute to the field of text classification by exploring the efficiency and usefulness of various categorization methods in automating the categorization process, with a specific focus on sentiment analysis and topic classification. There is a rising need for reliable and accurate text classification systems due to the exponential growth of textual data in a variety of fields. A wider range of users, including those who are not experts in Machine Learning (ML), can now use NLP technologies thanks to recent developments in the field of natural language processing (NLP). In accordance with this, the goal of this thesis is to compare and analyze the performance variations among various text categorization technologies when applied to automatically classify social media conversation, especially customer reviews for different products. By incorporating sentiment analysis and topic classification into the evaluation, this study aims to offer insights into the efficiency of various approaches in capturing the sentiment expressed in the text and identifying the underlying topics, improving the general understanding of text classification in the context of customer feedback analysis.
Author: Afumatu, Alexandra-Diana
Institution: Universitat Politècnica de Catalunya
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7056740664