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ORIGINAL RESEARCH article

Front. Psychol.
Sec. Emotion Science
Volume 15 - 2024 | doi: 10.3389/fpsyg.2024.1490796
This article is part of the Research Topic Methodology for Emotion-Aware Education Based on Artificial Intelligence View all 5 articles

A Sentiment Correlation Modeling-based Multi-label Text Sentiment Analysis Model

Provisionally accepted
YINGYING NI YINGYING NI 1Wei Ni Wei Ni 2*
  • 1 Shanghai Jiao Tong University, Shanghai, China
  • 2 Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, China

The final, formatted version of the article will be published soon.

    This paper proposes ECO-SAM, a sentiment correlation modeling-based multilabel sentiment analysis model.[Methods] ECO-SAM utilizes the pretrained BERT encoder to obtain semantic embeddings of input texts, then leverages the self-attention mechanism to model the semantic correlation between emotions. Finally, ECO-SAM utilizes a text-emotion matching neural network to make sentiment analysis for input texts.[Results] Experiment results in public datasets demonstrate that compared to baseline models, ECO-SAM obtains the precision score increasing by 13.33% at most, the recall score increasing by 3.69% at most, the F1 score increasing by 8.44% at most. Meanwhile, the modeled sentiment semantics are interpretable.[Limitations] The data modeled by ECO-SAM is limited to text modal, ignoring multimodal data that may enhance classification performance. The training data is not large scale, and there lack large-scale high-quality training data for fine-tuning sentiment analysis models.[Conclusions] ECO-SAM is capable for effectively modeling sentiment semantics and achieve excellent classification performance in many public sentiment analysis datasets.

    Keywords: text classification, sentiment analysis, Natural Language Processing, attention mechanism, Emotion Theory

    Received: 03 Sep 2024; Accepted: 04 Nov 2024.

    Copyright: © 2024 NI and Ni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Wei Ni, Department of Critical Care Medicine, Sir Run Run Shaw Hospital, Hangzhou, Zhejiang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.