Understanding public opinions on social media for financial sentiment analysis using AI-based techniques

The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the m...

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Main Authors: Qian, Cheng, Mathur, Nitya, Zakaria, Nor Hidayati, Arora, Rameshwar, Ali, Mazlan
Format: Article
Published: Elsevier Ltd. 2022
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Online Access:http://eprints.utm.my/id/eprint/100439/
http://dx.doi.org/10.1016/j.ipm.2022.103098
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spelling my.utm.1004392023-04-14T01:45:20Z http://eprints.utm.my/id/eprint/100439/ Understanding public opinions on social media for financial sentiment analysis using AI-based techniques Qian, Cheng Mathur, Nitya Zakaria, Nor Hidayati Arora, Rameshwar Ali, Mazlan HG Finance The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (∼ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding. Elsevier Ltd. 2022 Article PeerReviewed Qian, Cheng and Mathur, Nitya and Zakaria, Nor Hidayati and Arora, Rameshwar and Ali, Mazlan (2022) Understanding public opinions on social media for financial sentiment analysis using AI-based techniques. Information Processing and Management, 59 (6). pp. 1-15. ISSN 0306-4573 http://dx.doi.org/10.1016/j.ipm.2022.103098 DOI: 10.1016/j.ipm.2022.103098
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic HG Finance
spellingShingle HG Finance
Qian, Cheng
Mathur, Nitya
Zakaria, Nor Hidayati
Arora, Rameshwar
Ali, Mazlan
Understanding public opinions on social media for financial sentiment analysis using AI-based techniques
description The digital currency has taken the financial markets by storm ever since its inception. Academia and industry are focussing on Artificial intelligence (AI) tools and techniques to study and gain an understanding of how businesses can draw insights from the large-scale data available online. As the market is driven by public opinions, and social media today provides an encouraging platform to share ideas and views; organizations and policy-makers could use the natural language processing (NLP) technology of AI to analyze public sentiments. Recently, a new and moderately unconventional instrument known as non-fungible tokens (NFTs) is emerging as an upcoming business market. Unlike the stock market, no precise quantitative parameters exist for the price determination of NFTs. Instead, NFT markets are driven more by public opinion, expectations, the perception of buyers, and the goodwill of creators. This study evaluates human emotions on the social media platforms Twitter posted by the public relating to NFTs. Additionally, this study conducts secondary market analysis to determine the reasons for the growing acceptance of NFTs through sentiment and emotion analysis. We segregate tweets using Pearson Product-Moment Correlation Coefficient (PPMCC) and study 8-scale emotions (Anger, Anticipation, Disgust, Fear, Joy, Sadness, Surprise, and Trust) along with Positive and Negative sentiments. Tweets majorly contained positive sentiment (∼ 72%), and positive emotions like anticipation and trust were found to be predominant all over the world. This is the first of its kind financial and emotional analysis of tweets pertaining to NFTs to the best of our understanding.
format Article
author Qian, Cheng
Mathur, Nitya
Zakaria, Nor Hidayati
Arora, Rameshwar
Ali, Mazlan
author_facet Qian, Cheng
Mathur, Nitya
Zakaria, Nor Hidayati
Arora, Rameshwar
Ali, Mazlan
author_sort Qian, Cheng
title Understanding public opinions on social media for financial sentiment analysis using AI-based techniques
title_short Understanding public opinions on social media for financial sentiment analysis using AI-based techniques
title_full Understanding public opinions on social media for financial sentiment analysis using AI-based techniques
title_fullStr Understanding public opinions on social media for financial sentiment analysis using AI-based techniques
title_full_unstemmed Understanding public opinions on social media for financial sentiment analysis using AI-based techniques
title_sort understanding public opinions on social media for financial sentiment analysis using ai-based techniques
publisher Elsevier Ltd.
publishDate 2022
url http://eprints.utm.my/id/eprint/100439/
http://dx.doi.org/10.1016/j.ipm.2022.103098
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score 13.211869