Abstract
Using techniques such as machine learning (ML), natural language processing (NLP), data mining, and artificial intelligence (AI), sentiment analysis mines, extracts, and categorises user opinions provided on a business, product, person, service, event, or idea. The researchers utilised this emerging strategy to determine how social media users opinion on Tokyo 2022 Olympic and Paralympic Games. The main focus is on the fact that the Olympic Games in 2020 are significant when compared to other major international sporting events since they will be the first major gaming tournament to be staged after the pandemic. In this study, a novel approach known as content analysis and topic analysis were utilised. The main dataset utilised for this study is the tweets that used the hashtag #tokyo2022, the dataset composes of the data collected over the period of a week prior to the event. In total, 18,000 tweets were used. Here, a machine learning model is developed with the help of the tweepy.py module of the Python programming language. To collect data, the strategy of topic analysis was utilised, and the implementation of the AI technique is made possible by using the Twitter API. The collected data is then analysed by NVIVO AI to determine its theme, the polarity of its sentiment, and the frequency of its words. According to the data, there has been a favourable development in the situation and may assume that this is because of the pandemic caused by COVID-19.
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Sachdeva, P., Mitra, A. (2023). Decoding the Twitter Sentiment Using Artificial Intelligence Tools: A Study on Tokyo Olympics 2020. In: Ranganathan, G., Papakostas, G.A., Rocha, Á. (eds) Inventive Communication and Computational Technologies. ICICCT 2023. Lecture Notes in Networks and Systems, vol 757. Springer, Singapore. https://doi.org/10.1007/978-981-99-5166-6_37
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