Performance of Naïve Bayes in Sentiment Analysis of User Reviews Online
Habeebullah Shah Quadri1, R. K. Selvakumar2
1Mr. Habeebullah Shah Quadri, Student, Artificial Intelligence from CVR College of Engineering, Hyderabad, (Telangana), India.
2Dr. R. K. Selvakumar, Professor Department of Computer Science and Engineering , CVR College of Engineering, Hyderabad, (Telangana), India.
Manuscript received on November 10, 2020. | Revised Manuscript received on November 16, 2020. | Manuscript published on December 10, 2021. | PP: 64-68 | Volume-10 Issue-2, December 2020 | Retrieval Number: 100.1/ijitee.A81981110120| DOI: 10.35940/ijitee.A8198.1210220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Both sellers and buyers heavily depend on the opinions of customers in purchasing and selling products online. When it comes to text-based data, sentiment analysis of user reviews has become a prominent facet of machine learning. Text data is generally unstructured which makes opinion mining very challenging. A wide array of pre-processing and post-processing techniques need to be applied. But the major challenge is selecting the right classifier for the job. Naïve Bayes algorithm is a commonly used machine learning classifier when it comes to opinion mining and sentiment analysis. The focus of this survey is to observe and analyze the performance of Naïve Bayes algorithm in sentiment analysis of user reviews online. Recent research from a wide array of use-cases such as sentiment analysis of movie reviews, product reviews, book reviews, blog posts, microblogs and other sources of data have been taken into account. The results show that Naïve Bayes algorithm performs exceptionally well with accuracies between 75% to 99% across the board.
Keywords: Naïve Bayes Classifier, Multinomial NB, Bernoulli NB, Gaussian NB, Sentiment Analysis, User Reviews, Ecommerce.
Scope of the Article: Classification