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

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 usecase 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.

Automated summary

This study offers a novel approach by utilizing opinion retrieval theme along with sentiment analysis from social media platforms to support decision-making in product analysis and development. Google Glass is chosen as a case study, and a multi-task deep neural network architecture is designed for training sentiment prediction and opinion detection tasks.