Sentiment Analysis and Opinion Mining

A special issue of AI (ISSN 2673-2688).

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 4748

Special Issue Editor

Department of Public Health Sciences, University of North Carolina at Charlotte, 2901 University City Blvd., Charlotte, NC 28223, USA
Interests: mathematical biology; infectious diseases dynamics; epidemic analytics and modeling; data mining; machine learning and deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Understanding public opinions and accompanied sentiments towards many of today’s complex issues, such as politics, economics, and public health emergencies (e.g., the current COVID-19 pandemic), is paramount to effectively and efficiently extract key insights from the public, develop corresponding actions, and cope with various types of misinformation surrounding these issues.

Recent advances in deep learning (DL), natural language processing (NLP), and computational linguistics have made significant contributions to opinion mining and sentiment analysis. We are pleased to invite you to contribute to this Special Issue of Sentiment Analysis and Opinion Mining, which aims to further leverage our scientific understanding and technical advances of public opinions and sentiments, especially from large online datasets (e.g., social media), via novel computational techniques.

In this Special Issue of Sentiment Analysis and Opinion Mining, we welcome original research articles and reviews, which may focus on the following research areas:

  • Data mining;
  • Opinion mining;
  • Sentiment analysis;
  • Social media analytics;
  • Natural language processing (NLP) ;
  • Computational linguistics;
  • Web mining;
  • Deep learning.

I look forward to receiving your contributions.

Dr. Shi Chen
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AI is an international peer-reviewed open access quarterly journal published by MDPI.

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Keywords

  • data mining
  • opinion mining
  • sentiment analysis
  • social media analytics
  • natural language processing (NLP)
  • computational linguistics
  • web mining
  • deep learning

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Published Papers (1 paper)

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Research

15 pages, 709 KiB  
Article
Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance
by Tianyi Xie, Yaorong Ge, Qian Xu and Shi Chen
AI 2023, 4(1), 333-347; https://doi.org/10.3390/ai4010016 - 17 Mar 2023
Cited by 5 | Viewed by 3767
Abstract
Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance [...] Read more.
Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context. Full article
(This article belongs to the Special Issue Sentiment Analysis and Opinion Mining)
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