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ABET: an affective emotion-topic method of biterms for emotion recognition from the short texts

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Abstract

Nowadays, online users write short messages to share their feelings on social networking sites, such as discussion forums, question answering websites, etc., making these sites very popular. The increase in these short-term messages causes a huge data sparsity, making emotion recognition a challenging task. Therefore, a word co-occurrence pattern called biterms is generated from a large-scale dataset to prevent severe data sparsity issues. The topic modeling algorithms and acceleration algorithms are implemented to extract more reliable topics from the group of terms. Based on biterm technique, in this paper, a new algorithm called "Affected biterm emotion topic" is proposed for emotion recognition from a short text. For the experimental purpose, two popular short text datasets, SemEval and International Survey on Emotion Antecedents and Reactions (ISEAR), are used to investigate the performance of the proposed algorithm with the benchmark methods light latent dirichlet allocation (LLDA), biterm topic model (BTM), emotion-topic model (ETM), contextual sentiment topic model (CSTM), Sentiment latent topic model (SLTM) and siasme network based supervised topic model (SNSTM). The proposed algorithm is evaluated using the benchmark methods for mean, variance, and accuracy. The experimental result shows that the proposed algorithm is effective in analyzing emotions.

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Acknowledgements

The authors acknowledge the support given by the faculties and staff of the Department of Information Technology, VSSUT, Burla

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  1. Department of Information Technology, Veer Surendra Sai University of Technology, Burla, India

    Anima Pradhan, Manas Ranjan Senapati & Pradip Kumar Sahu

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  1. Anima Pradhan
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  2. Manas Ranjan Senapati
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  3. Pradip Kumar Sahu
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All the authors have equally contributed to preparing the manuscript.

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Correspondence to Manas Ranjan Senapati.

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Pradhan, A., Senapati, M.R. & Sahu, P.K. ABET: an affective emotion-topic method of biterms for emotion recognition from the short texts. J Ambient Intell Human Comput 14, 13451–13463 (2023). https://doi.org/10.1007/s12652-022-03799-9

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  • DOI: https://doi.org/10.1007/s12652-022-03799-9

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