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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Employing Machine Learning for book Review classification |
| Authors Name: | Jagadeesh Thotapola , Vamsi Chatla , Indhu Priya Jarpula |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_191689
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| Published Paper Id: | IJNRD2305231 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Sentiment analysis, which is also known as opinion mining, is an essential task in Natural Language Processing (NLP). Over the past few years, there has been a growing interest in sentiment analysis, which involves classifying text to determine the intended meaning for the end user. One of the most valuable sources of consumer opinion is online book reviews, which are crucial in evaluating the quality of the book's content. To assist users in making informed decisions about which books to read, online review tools are now available. This paper explores various preprocessing techniques, such as removing HTML tags, URLs, punctuation, whitespace, special characters, and stemming, to eliminate noise. Additionally, machine learning algorithms are used for sentiment analysis to categorize book reviews and make recommendations based on user interests. By classifying user reviews as either positive or negative, clustering algorithms can be used to group people based on their interests, and a collaborative approach can be used to recommend books. The study aims to categorize book reviews using sentiment analysis and make book recommendations based on user interest variables. To achieve the most accurate results in the least amount of time, book feature sentiment must be extracted. This paper compares various levels of sentiment analysis and different approaches currently used to develop book recommendation systems. |
| Keywords: | Sentiment Analysis, Natural Language Processing, Machine Learning, Book Reviews, Recommendation Systems, and Clustering. |
| Cite Article: | "Employing Machine Learning for book Review classification", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.c229-c233, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305231.pdf |
| Downloads: | 000118758 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305231 Registration ID: 191689 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: c229-c233 Country: khammam district, Telangana, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305231 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305231 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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