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Heterogeneous classifier ensemble for sentiment analysis of Bengali and Hindi tweets

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Abstract

Sentiment analysis is an essential step for analysing social media texts such as tweets and other posts on the various micro-blogging sites. The basic step of sentiment analysis is sentiment polarity detection, which identifies whether an input piece of social media text is positive, negative or neutral. In this paper, we present an approach that combines heterogeneous classifiers in an ensemble for sentiment polarity detection in Bengali and Hindi tweets. Our proposed method constructs an ensemble of three different base classifiers where the feature set for each base classifier is different from each other. We have also incorporated an external knowledge base called sentiment lexicon to augment tweet words with sentiment polarity information retrieved from the sentiment lexicon. Experimental results show the effectiveness of our proposed heterogeneous ensemble model for sentiment polarity detection for both Bengali and Hindi languages. It has been shown that our system outperforms other existing Bengali and Hindi sentiment classification systems to which it is compared.

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Notes

  1. http://amitavadas.com/sentiwordnet.php

  2. http://www.cs.waikato.ac.nz/ml/weka/

  3. https://radimrehurek.com/gensim/models/word2vec.html

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Acknowledgements

This research work is supported by the project titled “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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  1. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Kamal Sarkar

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  1. Kamal Sarkar
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Correspondence to Kamal Sarkar.

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Sarkar, K. Heterogeneous classifier ensemble for sentiment analysis of Bengali and Hindi tweets. Sādhanā 45, 196 (2020). https://doi.org/10.1007/s12046-020-01424-z

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