Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study
As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to unders...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/6/1731 |
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| author | Yujia Liu Dong Zhang Qian Wan Zhongzhen Lin |
| author_facet | Yujia Liu Dong Zhang Qian Wan Zhongzhen Lin |
| author_sort | Yujia Liu |
| collection | DOAJ |
| description | As the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries. |
| format | Article |
| id | doaj-art-d16db91a34fb41afbb2a4603c08d8c63 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-d16db91a34fb41afbb2a4603c08d8c632025-08-20T01:48:49ZengMDPI AGSensors1424-82202025-03-01256173110.3390/s25061731Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case StudyYujia Liu0Dong Zhang1Qian Wan2Zhongzhen Lin3School of Art and Design, Guangdong University of Technology, Guangzhou 510090, ChinaShenzhen Lingdong Software Development Co., Ltd., Shenzhen 518064, ChinaSchool of Art and Design, Guangdong University of Technology, Guangzhou 510090, ChinaSchool of Art and Design, Guangdong University of Technology, Guangzhou 510090, ChinaAs the automotive industry advances rapidly, user needs are in a constant state of evolution. Driven by advancements in big data, artificial intelligence, and natural language processing, mining user requirements from user-generated content (UGC) on social media has become an effective way to understand these dynamic needs. While existing technologies have progressed in topic identification and sentiment analysis, single-method approaches often face limitations. This study proposes a novel method for user requirement mining based on BERTopic and RoBERTa, combining the strengths of topic modeling and sentiment analysis to provide a more comprehensive analysis of user needs. To validate this approach, UGC data from four major Chinese media platforms were collected. BERTopic was applied for topic extraction and RoBERTa for sentiment analysis, facilitating a linked analysis of user emotions and identified topics. The findings categorize user requirements into four main areas—performance, comfort and experience, price sensitivity, and safety—while also reflecting the increasing relevance of advanced features, such as sensors, powertrain performance, and other technologies. This method enhances user requirement identification by integrating sentiment analysis with topic modeling, offering actionable insights for automotive manufacturers in product optimization and marketing strategies and presenting a scalable approach adaptable across various industries.https://www.mdpi.com/1424-8220/25/6/1731user requirementdata mininguser-generated content (UGC)topic identificationsentiment analysis |
| spellingShingle | Yujia Liu Dong Zhang Qian Wan Zhongzhen Lin Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study Sensors user requirement data mining user-generated content (UGC) topic identification sentiment analysis |
| title | Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study |
| title_full | Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study |
| title_fullStr | Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study |
| title_full_unstemmed | Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study |
| title_short | Innovative Mining of User Requirements Through Combined Topic Modeling and Sentiment Analysis: An Automotive Case Study |
| title_sort | innovative mining of user requirements through combined topic modeling and sentiment analysis an automotive case study |
| topic | user requirement data mining user-generated content (UGC) topic identification sentiment analysis |
| url | https://www.mdpi.com/1424-8220/25/6/1731 |
| work_keys_str_mv | AT yujialiu innovativeminingofuserrequirementsthroughcombinedtopicmodelingandsentimentanalysisanautomotivecasestudy AT dongzhang innovativeminingofuserrequirementsthroughcombinedtopicmodelingandsentimentanalysisanautomotivecasestudy AT qianwan innovativeminingofuserrequirementsthroughcombinedtopicmodelingandsentimentanalysisanautomotivecasestudy AT zhongzhenlin innovativeminingofuserrequirementsthroughcombinedtopicmodelingandsentimentanalysisanautomotivecasestudy |