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Public Opinions on ChatGPT: An Analysis of Reddit Discussions by Using Sentiment Analysis, Topic Modeling, and SWOT Analysis 公众对 ChatGPT 的看法:利用情感分析、话题建模和 SWOT 分析对 Reddit 讨论进行分析
Data Intelligence Pub Date : 2024-03-29 DOI: 10.1162/dint_a_00250
Shwe Zin Su Naing, Piyachat Udomwong
{"title":"Public Opinions on ChatGPT: An Analysis of Reddit Discussions by\u0000 Using Sentiment Analysis, Topic Modeling, and SWOT Analysis","authors":"Shwe Zin Su Naing, Piyachat Udomwong","doi":"10.1162/dint_a_00250","DOIUrl":"https://doi.org/10.1162/dint_a_00250","url":null,"abstract":"\u0000 The sudden arrival of AI (Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.","PeriodicalId":505217,"journal":{"name":"Data Intelligence","volume":"20 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140366756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Optimizing ASReview simulations: A generic multiprocessing solution for ‘light-data’ and ‘heavy-data’ users 优化 ASReview 仿真:适用于 "轻数据 "和 "重数据 "用户的通用多处理解决方案
Data Intelligence Pub Date : 2024-01-19 DOI: 10.1162/dint_a_00244
Sergei Romanov, Abel Soares Siqueira, Jonathan de Bruin, J. Teijema, Laura Hofstee, R. van de Schoot
{"title":"Optimizing ASReview simulations: A generic multiprocessing solution for ‘light-data’ and ‘heavy-data’ users","authors":"Sergei Romanov, Abel Soares Siqueira, Jonathan de Bruin, J. Teijema, Laura Hofstee, R. van de Schoot","doi":"10.1162/dint_a_00244","DOIUrl":"https://doi.org/10.1162/dint_a_00244","url":null,"abstract":"\u0000 Active learning can be used for optimizing and speeding up the screening phase of systematic reviews. Running simulation studies mimicking the screening process can be used to test the performance of different machine-learning models or to study the impact of different training data. This paper presents an architecture design with a multiprocessing computational strategy for running many such simulation studies in parallel, using the ASReview Makita workflow generator and Kubernetes software for deployment with cloud technologies. We provide a technical explanation of the proposed cloud architecture and its usage. In addition to that, we conducted 1140 simulations investigating the computational time using various numbers of CPUs and RAM settings. Our analysis demonstrates the degree to which simulations can be accelerated with multiprocessing computing usage. The parallel computation strategy and the architecture design that was developed in the present paper can contribute to future research with more optimal simulation time and, at the same time, ensure the safe completion of the needed processes.","PeriodicalId":505217,"journal":{"name":"Data Intelligence","volume":"16 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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