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Aspect-Based Opinion Mining: A Framework for Spam and Ham Review Detection
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Author(s): Neelam Rani (Poornima University, Jaipur, India), Devendra Kumar Somwanshi (Poornima University, Jaipur, India)and Archika Jain (Swami Keshvanand Institute of Technology, Management, and Gramothan, Jaipur, India)
Copyright: 2026
Pages: 24
Source title:
Detecting Hate Speech in Human and AI-Generated Content: Techniques, Bias Mitigation, and Ethical Considerations
Source Author(s)/Editor(s): Mohammad Arsalan (Qatar University, Qatar), Mehul Mahrishi (Swami Keshvanand Institute of Technology, India), Ruchi Doshi (Universidad Azteca, Chalco, Mexico), Archika Jain (Swami Keshvanand Institute of Technology, India)and Chandrashekhar Goswami (Sir Padampat Singhania University, India)
DOI: 10.4018/979-8-3373-3063-1.ch011
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Abstract
Sentiment Analysis deals with analyzing users comment on a particular thing and then computationally figuring out the actual sentiment of the user i.e Positive or Negative. The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive or negative. This chapter presents a comprehensive review of over 50 papers on sentiment analysis, featuring a comparative analysis of various research techniques. The review highlights strengths and weaknesses of existing methods, identifies research gaps, and provides a foundation for future research directions. In this chapter Naive Bayes approach is used for sentiment prediction, specifically for SPAM and HAM detection. The prediction model is trained using the Naive Bayes algorithm, and the training set is fitted using the WordNet library.
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