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A Review of Text Mining and Sentiment Analysis for the Purpose of Determining the Veracity of Online Reviews
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Author(s): P. S. Venkateswaran (PSNA College of Engineering and Technology, India), M. Lishmah Dominic (PSNA College of Engineering and Technology, India), Deepak Kem (Dr. K.R. Narayanan Centre for Dalit and Minorities Studies, India), Niranchana Shri Viswanathan (Sapthagiri NPS University, India), Amit Kumar Kashyap (Nirma University, India), S. R. Rameshkkumar (Sir Padampat Singhania University, India)and R. Sivakani (Dhaanish Ahmed College of Engineering, India)
Copyright: 2025
Pages: 22
Source title:
E-Commerce, Marketing, and Consumer Behavior in the AI Era
Source Author(s)/Editor(s): Ahmed J. Obaid (University of Kufa, Iraq), Adriana Burlea-Schiopoiu (University of Craiova, Romania), Bharat Bhushan (Sharda University, India), Sobirov Bobur (Tashkent State University Economics, Uzbekistan)and S. Suman Rajest (Dhaanish Ahmed College of Engineering, India)
DOI: 10.4018/979-8-3693-5548-0.ch012
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Abstract
Text mining is crucial in extracting valuable insights from unstructured text data, such as emails, social media posts, and consumer comments. By analyzing large datasets, organizations can uncover hidden patterns and trends that provide actionable business intelligence. This process involves several key steps, including data cleaning to remove irrelevant information, identifying significant keywords that highlight important themes, and structuring the data in a way that facilitates analysis. In marketing analytics, business intelligence derived from text mining enables informed decision-making based on detailed consumer insights. Detecting fake reviews across various platforms and industries is particularly critical today. Methods for identifying counterfeit feedback involve examining corporate achievements, understanding customer purchasing behaviours, managing brand perception, and utilizing emotional intelligence to gauge authenticity.
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