Text mining to understand the influence of social media applications on smartphone supply chain

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Abstract

Social media applications are being currently utilized to influence the information flow aspect of supply chain management. Verifiable real-time data can provide timely and insightful information about several key aspects of the supply chain of a product and enable it to adapt quickly to ever-changing market conditions. In this paper, information is gathered from Twitter to understand how the tweets about a given smartphone can influence its supply chain and its management. Based on relevant hashtags and keywords found in the latest news about three different smartphone brands (i.e. Apple, Samsung, Huawei), data mining is used to extract and analyze the tweets with the specific hashtags or keywords from Twitter. To reduce the loss of a significant amount of event related information due to Twitter's APIs data access restrictions, the concept of refined hashtags and Keywords is also used to enhance the Twitter crawling model used. Sentiment analysis and opinion analysis were carried out based on the refined hashtags with a goal of analyzing people's emotion towards a specific smartphone brand and on the possibility to predict its influence on aspects of supply chain for enabling real-time adjustments for ensure a robust supply chain model. This effort enabled identifying a new model of smartphone supply chain management with built in social media information flow.

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APA

Akundi, A., Tseng, B., Wu, J., Smith, E., Subbalakshmi, M., & Aguirre, F. (2018). Text mining to understand the influence of social media applications on smartphone supply chain. In Procedia Computer Science (Vol. 140, pp. 87–94). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.10.296

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