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How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling

How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling

Dwijendra Nath Dwivedi, Ghanashyama Mahanty, Anilkumar Vemareddy
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 14
ISSN: 2155-6377|EISSN: 2155-6385|EISBN13: 9781683182085|DOI: 10.4018/IJIRR.298646
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MLA

Dwivedi, Dwijendra Nath, et al. "How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling." IJIRR vol.12, no.1 2022: pp.1-14. http://doi.org/10.4018/IJIRR.298646

APA

Dwivedi, D. N., Mahanty, G., & Vemareddy, A. (2022). How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling. International Journal of Information Retrieval Research (IJIRR), 12(1), 1-14. http://doi.org/10.4018/IJIRR.298646

Chicago

Dwivedi, Dwijendra Nath, Ghanashyama Mahanty, and Anilkumar Vemareddy. "How Responsible Is AI?: Identification of Key Public Concerns Using Sentiment Analysis and Topic Modeling," International Journal of Information Retrieval Research (IJIRR) 12, no.1: 1-14. http://doi.org/10.4018/IJIRR.298646

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Abstract

Many businesses around the World are adopting AI with the hope of increasing their top-line and bottom-line numbers. The COVID19 pandemic has further accelerated the journey. While AI technology promising to bring enormous benefits, the challenges come in similar proportions. In the current form, the requirements for transparency and trust are relatively low for AI systems. On the other hand, there is a lot of regulatory pressure for AI systems to be trustworthy and responsible. Challenges still exist both on the methods and theory side and how explanations are used in practice. The objective of this paper is to analyze Twitter data to extract sentiments and opinions in unstructured text. We attempted to use contextual text analytics to categorize the twitter data to understand the positive or negative sentiments and feelings for the AI Ethical challenges and highlight the key concerns. Text clustering has also been performed on positive and negative sentiments to understand the key themes behind people's concern.