Thematic Analysis for Text Review Detection Using Machine Learning
Riya Bhunia, Ankita Chakraborty, Soumen Santra, Anirban Sarkar, Subrata Jana, Santanu Dasgupta · 2025
Nowadays online reviews written by users have become incredibly valuable, for both consumers and businesses as a source of information. Thematic analysis, which is a research method, has gained recognition as a tool to extract meaningful insights from these reviews. In this abstract we provide an overview of how thematic analysis applied in the context of review detection. Thematic analysis involves an approach to identify, analyze, and report patterns within data. It proves to be immensely useful in understanding the sentiments, opinions, and experiences expressed in user reviews. This method encompasses steps such as data collection becoming familiar with the data, coding, identifying themes, and interpretation. By utilizing analysis in review detection businesses can gain structured and systematic access to valuable insights from user generated content. By comprehending the themes and sentiments conveyed in reviews companies can make decisions that improve customer experiences and enhance their products or services. As our digital world continues to evolve the significance of analysis in review detection grows even more crucial for enabling businesses to remain competitive. The process of manually searching extensive information datasets can be time-consuming and cumbersome. This abstract explores the integration of word clouds as a visual resource to enhance the thematic search process. Word clouds provide visibility into word frequency, allowing researchers to quickly gain insight into the most important themes and concepts in their data. It plays a role in uncovering insights from the vast amount of user generated content available online while empowering businesses, with data driven decisions that boost customer satisfaction.