Multi-class Regret Detection in Hindi Devanagari Script

• URL: http://arxiv.org/abs/2401.16561v1
• Date: Mon, 29 Jan 2024 20:58:43 GMT
• Title: Multi-class Regret Detection in Hindi Devanagari Script
• Authors: Renuka Sharma, Sushama Nagpal, Sangeeta Sabharwal, Sabur Butt
• Abstract summary: This study focuses on regret and how it is expressed, specifically in Hindi, on various social media platforms. We present a novel dataset from three different sources, where each sentence has been manually classified into one of three classes "Regret by action", "Regret by inaction", and "No regret" Our findings indicate that individuals on social media platforms frequently express regret for both past inactions and actions.
• Score: 1.249418440326334
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The number of Hindi speakers on social media has increased dramatically in recent years. Regret is a common emotional experience in our everyday life. Many speakers on social media, share their regretful experiences and opinions regularly. It might cause a re-evaluation of one's choices and a desire to make a different option if given the chance. As a result, knowing the source of regret is critical for investigating its impact on behavior and decision-making. This study focuses on regret and how it is expressed, specifically in Hindi, on various social media platforms. In our study, we present a novel dataset from three different sources, where each sentence has been manually classified into one of three classes "Regret by action", "Regret by inaction", and "No regret". Next, we use this dataset to investigate the linguistic expressions of regret in Hindi text and also identify the textual domains that are most frequently associated with regret. Our findings indicate that individuals on social media platforms frequently express regret for both past inactions and actions, particularly within the domain of interpersonal relationships. We use a pre-trained BERT model to generate word embeddings for the Hindi dataset and also compare deep learning models with conventional machine learning models in order to demonstrate accuracy. Our results show that BERT embedding with CNN consistently surpassed other models. This described the effectiveness of BERT for conveying the context and meaning of words in the regret domain.

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