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Social media mining for ideation: Identification of sustainable solutions and opinions

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  • Ozcan, Sercan
  • Suloglu, Metin
  • Sakar, C. Okan
  • Chatufale, Sushant

Abstract

The availability of social media-based data creates opportunities to obtain information about consumers, trends, companies and technologies using text mining techniques. However, the quality of the data is a significant concern for social media-based analyses. The aim of this study was to mine tweets (microblogs) to explore trends and retrieve ideas for various purposes such as product development, technology and sustainability-oriented considerations. The core methodological approach was to create a classification model to identify tweets that contained an idea. This classification model was used as a pre-processing step so the query results obtained from the application programming interface were cleared from the messages that contained the search terms used in the query but did not contain an idea. The results of this study demonstrate that our method based on text mining, and supervised or semi-supervised classification methods, can extract ideas from social media. The social media data mining process illustrated in our study can be utilised as a decision-making tool to detect innovative ideas or solutions about a product or service and summarise them into meaningful clusters. We believe that our findings are significant for the sustainability, tech mining and innovation management communities.

Suggested Citation

  • Ozcan, Sercan & Suloglu, Metin & Sakar, C. Okan & Chatufale, Sushant, 2021. "Social media mining for ideation: Identification of sustainable solutions and opinions," Technovation, Elsevier, vol. 107(C).
    • Handle: RePEc:eee:techno:v:107:y:2021:i:c:s0166497221001036
    DOI: 10.1016/j.technovation.2021.102322
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    2. Saura, Jose Ramon & Palacios-Marqués, Daniel & Ribeiro-Soriano, Domingo, 2023. "Exploring the boundaries of open innovation: Evidence from social media mining," Technovation, Elsevier, vol. 119(C).
    3. Vincenzo Varriale & Antonello Cammarano & Francesca Michelino & Mauro Caputo, 2023. "Industry 5.0 and Triple Bottom Line Approach in Supply Chain Management: The State-of-the-Art," Sustainability, MDPI, vol. 15(7), pages 1-30, March.
    4. Chen, Qian & Magnusson, Mats & Björk, Jennie, 2023. "Selection bias of ideas for sustainability-oriented innovation in internal crowdsourcing," Technovation, Elsevier, vol. 124(C).
    5. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    6. Han, Myat Su & Ma, Shuang (Sara) & Wang, Yonggui & Tian, Qinghong, 2023. "Impact of technology-enabled product eco-innovation: Empirical evidence from the Chinese manufacturing industry," Technovation, Elsevier, vol. 128(C).
    7. Zhan, Yuanzhu & Xiong, Yu & Xing, Xinjie, 2023. "A conceptual model and case study of blockchain-enabled social media platform," Technovation, Elsevier, vol. 119(C).

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