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.

社交媒体平台被认为是公司与消费者互动的最有效中介之一。营销领域基于社交媒体的决策支持系统已经高度发达，但产品开发和创新导向的研究仍然有限。本研究提供了一种新颖的方法，它利用意见检索主题和情感分析来支持产品分析和开发的决策过程。为了实现这一目标，我们提出了一种基于端到端社交媒体的意见检索系统，并利用机器学习和自然语言处理技术。谷歌眼镜被选为用例，因为该产品尽管提供了卓越的技术产品，但仍无法实现其商业目标。我们设计了一个多任务深度神经网络架构来训练情感预测和意见检测任务。我们首先根据情感标签将包含某些有用意见和建议的推文分为两类。分析负面推文以识别与产品相关的问题，而正面和中立的推文用于提取创新想法并识别产品开发的新用例。我们可视化并解释从每个情感标签组中提取的关键字集群。除了方法论的贡献外，这项研究还为下一代智能眼镜做出了实际贡献。分析负面推文以识别与产品相关的问题，而正面和中立的推文用于提取创新想法并识别产品开发的新用例。我们可视化并解释从每个情感标签组中提取的关键字集群。除了方法论的贡献外，这项研究还为下一代智能眼镜做出了实际贡献。分析负面推文以识别与产品相关的问题，而正面和中立的推文用于提取创新想法并识别产品开发的新用例。我们可视化并解释从每个情感标签组中提取的关键字集群。除了方法论的贡献外，这项研究还为下一代智能眼镜做出了实际贡献。