An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews

Research output: Contribution to journalArticlepeer-review


Sentiment analysis has demonstrated its value in a range of high-stakes domains. From financial markets to supply chain management, logistics, and technology legitimacy assessment, sentiment analysis offers insights into public sentiment, actionable data, and improved decision forecasting. This study contributes to this growing body of research by offering a novel multi-view deep learning approach to sentiment analysis that incorporates non-textual features like emojis. The proposed approach considers both textual and emoji views as distinct views of emotional information for the sentiment classification model, and the results acknowledge their individual and combined contributions to sentiment analysis. Comparative analysis with baseline classifiers reveals that incorporating emoji features significantly enriches sentiment analysis, enhancing the accuracy, F1-score, and execution time of the proposed model. Additionally, this study employs LIME for explainable sentiment analysis to provide insights into the model's decision-making process, enabling high-stakes businesses to understand the factors driving customer sentiment. The present study contributes to the literature on multi-view text classification in the context of social media and provides an innovative analytics method for businesses to extract valuable emotional information from electronic word of mouth (eWOM), which can help them stay ahead of the competition in a rapidly evolving digital landscape. In addition, the findings of this paper have important implications for policy development in digital communication and social media monitoring. Recognizing the importance of emojis in sentiment expression can inform policies by helping them better understand public sentiment and tailor policy solutions that better address the concerns of the public.
Original languageEnglish
Article number123326
Number of pages23
JournalTechnological Forecasting and Social Change
Early online date16 Mar 2024
Publication statusPublished - May 2024

Bibliographical note

Copyright © 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (

Data Access Statement

The datasets generated and analyzed during this study are available in the Kaggle repository:

Sentiment 140 Dataset:

Emoji Tweet Dataset:


  • Explainable sentiment analysis
  • Multi-view learning
  • High-stakes decision forecasting
  • Marketing analytics
  • Social media reviews


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