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Social media-based opinion retrieval for product analysis using multi-task deep neural networks

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Social media platforms are considered one of the most effective intermediaries for companies to interact with consumers. Social media-based decision support systems for the marketing domain are highly developed, but product development and innovation-oriented studies remain limited. This study offers a novel approach which utilises opinion retrieval theme along with sentiment analysis to support the decision-making process for product analysis and development. To achieve this aim, we propose an end-to-end social media-based opinion retrieval system and utilise machine learning and natural language processing techniques. Google Glass is chosen as a use-case as this product was unable to achieve its commercial targets despite its superior technological offerings. We design a multi-task deep neural network architecture for the training of sentiment prediction and opinion detection tasks. We first divide the tweets containing certain useful opinions and suggestions into two categories based on their sentiment labels. The negative tweets are analysed to identify product-related concerns, whereas the positive and neutral tweets are used to extract innovative ideas and identify new use cases for product development. We visualise and interpret the clusters of keywords extracted from each sentiment label group . Apart from methodological contributions, this study offers practical contributions for the next generations of smart glasses.
Original languageEnglish
Article number115388
Number of pages13
JournalExpert Systems with Applications
Volume183
Early online date12 Jun 2021
DOIs
Publication statusEarly online - 12 Jun 2021

Documents

  • Social Media-Based Opinion Retrieval for Product Analysis Using Multi-Task Deep Neural Networks

    Accepted author manuscript (Post-print), 1.44 MB, PDF document

    Due to publisher’s copyright restrictions, this document is not freely available to download from this website until: 12/06/22

    Licence: CC BY-NC-ND

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