Multimodal, multiview and multitasking depression detection framework endorsed with auxiliary sentiment polarity and emotion detection

Shelley Gupta (), Archana Singh () and Jayanthi Ranjan ()
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Shelley Gupta: Amity School of Engineering and Technology
Archana Singh: Amity School of Engineering and Technology
Jayanthi Ranjan: Sharda University

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 1, No 23, 337-352

Abstract: Abstract The impact of online social media has aided the users in sharing of knowledge, mood, feelings, and interests to the large volume of audience. The mental health of a person can be easily identified by analysing these expressions consisting of different modalities (text and emojis/emoticons). This research work aims to investigate the mood disorder like depression, low mood and other symptoms using tweets and emoticons. The present work curated the twitter based SentiEmoDD dataset as a benchmark for depression detection, labelled with sentiments analysis, emotions detection and other symptoms important for depression detection. The evolved dataset is equipped with both modalities (text and emojis) of tweets. A novel approach has been proposed based on the multi-view ensemble learning model contemplated to attain the information available in different modalities of a sentence for better depression detection. The proposed approach extracts the results from inter ensemble learning model and intra ensemble learning model. The experimental results clearly indicates that multimodal, multi-view and multitasking proposed framework provides an accuracy of 88.29% for the primary task of depression detection SVM linear kernel function. The stacking technique used here, provides the accuracy of 87.69% to detect depression using the proposed algorithm considering all the expressions of emoji and text combinations.

Keywords: Depression detection; Sentiment analysis; Emotion detection; Multiview; Multimodal; Multitasking (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13198-023-01861-z

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