Sentiment Analysis of Customer Review: An Approach for Book Selection in Library
Keywords:
Sentiment Analysis, Opinion Mining, Natural language processing, Customer feedback, Book selection processAbstract
The rapid growth of online customer reviews has created new opportunities for libraries to utilize user-generated feedback for informed collection development. This study applies sentiment analysis techniques to examine customer opinions on books in order to support effective library book selection decisions. Reviews were collected from the Amazon.in platform for two popular self-help books, The Power of Your Subconscious Mind by Joseph Murphy and The Power of a Positive Attitude: Your Road to Success by Roger Fritz. A lexicon-based sentiment analysis approach was implemented using the R programming environment. The dataset was pre-processed through tokenization, stop-word removal, stemming, lemmatization, and normalization to enhance data quality. Emotional dimensions such as joy, trust, anger, fear, sadness, and overall polarity were computed to evaluate customer sentiment. The results indicate that both books achieved strongly positive sentiment scores, reflecting high levels of user satisfaction and acceptance. The findings demonstrate that sentiment analysis can efficiently process large volumes of textual review data and serve as a valuable decision-support tool for librarians. This study highlights the practical application of open-source text mining techniques in strengthening evidence-based library collection management.
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