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A Stacked Ensemble Approach to Bengali Sentiment Analysis

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Intelligent Human Computer Interaction (IHCI 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11886))

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Abstract

Sentiment analysis is a crucial step in the social media data analysis. The majority of research works on sentiment analysis focus on sentiment polarity detection which identifies whether an input text is positive, negative or neutral. In this paper, we have implemented a stacked ensemble approach to sentiment polarity detection in Bengali tweets. The basic concept of stacked generalization is to fuse the outputs of the first level base classifiers using a second-level Meta classifier in an ensemble. In our ensemble method, we have used two types of base classifiers- multinomial Naïve Bayes classifiers and SVM that make use of a diverse set of features. Our proposed approach shows an improvement over some existing Bengali sentiment analysis approaches reported in the literature.

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Acknowledgments

This research work has received support from the project entitled ‘‘Indian Social Media Sensor: an Indian Social Media Text Mining System for Topic Detection, Topic Sentiment Analysis and Opinion Summarization’’ funded by the Department of Science and Technology, Government of India under the SERB scheme.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Jadavpur University, Kolkata, 700032, India

    Kamal Sarkar

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  1. Kamal Sarkar
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Correspondence to Kamal Sarkar .

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Editors and Affiliations

  1. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  2. Indian Institute of Technology, Jodhpur, India

    Santanu Chaudhury

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Sarkar, K. (2020). A Stacked Ensemble Approach to Bengali Sentiment Analysis. In: Tiwary, U., Chaudhury, S. (eds) Intelligent Human Computer Interaction. IHCI 2019. Lecture Notes in Computer Science(), vol 11886. Springer, Cham. https://doi.org/10.1007/978-3-030-44689-5_10

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  • DOI: https://doi.org/10.1007/978-3-030-44689-5_10

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  • Online ISBN: 978-3-030-44689-5

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