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Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database

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Intelligent Information and Database Systems (ACIIDS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13757))

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Abstract

Sentiment analysis (SA), often known as opinion mining, is the subjective examination of a written text. Moreover, SA is a critical technique in today's artificial intelligence (AI) field for extracting emotional information from huge amounts of data. The study is based on the Internet Movie Database (IMDB) dataset, which comprises movie reviews and the positive or negative labels that are connected with them. Our research experiment's objective is to identify the model with the best accuracy and the most generality. Text preprocessing is the first and most critical phase in a Natural Language Processing (NLP) system since it significantly impacts the overall accuracy of the classification algorithms. The experiment implements the Term Frequency-Inverse Document Frequency model (TFIDF) to feature selection and extractions. The following classifiers are used in this work: Linear Model and Naïve Bayes. Besides, we explore the possible options of loss functions such as square_hinge, huber, modified_huber, log, epsilon_insensitive, perceptron, and modified_huber. ComplementNB achieves the highest accuracy, 75.13%, for both classification reports based on our experiment result.

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Acknowledgment

This paper is supported by the Ministry of Science and Technology, Taiwan. The Nos are MOST-107-2221-E-324-018-MY2 and MOST-109-2622-E-324-004, Taiwan.

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Authors and Affiliations

  1. Department of Information Management, Chaoyang University of Technology, Taichung, Republic of China

    Christine Dewi & Rung-Ching Chen

  2. Faculty of Information Technology, Satya Wacana Christian University, Salatiga, Indonesia

    Christine Dewi

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  1. Christine Dewi
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  2. Rung-Ching Chen
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Correspondence to Rung-Ching Chen .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Vietnam National University, Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Tien Khoa Tran

  3. Al-Farabi Kazakh National University, Almaty, Kazakhstan

    Ualsher Tukayev

  4. National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  5. Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  6. University of Newcastle, Newcastle, NSW, Australia

    Edward Szczerbicki

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Dewi, C., Chen, RC. (2022). Complement Naive Bayes Classifier for Sentiment Analysis of Internet Movie Database. In: Nguyen, N.T., Tran, T.K., Tukayev, U., Hong, TP., Trawiński, B., Szczerbicki, E. (eds) Intelligent Information and Database Systems. ACIIDS 2022. Lecture Notes in Computer Science(), vol 13757. Springer, Cham. https://doi.org/10.1007/978-3-031-21743-2_7

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  • DOI: https://doi.org/10.1007/978-3-031-21743-2_7

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