Text Mining of Fitbit`s Twitter Data-Gaining Business Insights |
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Text or unstructured data comprised about 80% of the data generated in various areas including business, research, and life science. However, the nature of that data was such that it was a challenge to understand and derive knowledge from it in order for the management to make informed decisions during data analysis. Due to the enormous quantity of the data, it was a complex process for data analysts to conduct statistical analysis on it.
It was observed that the pace at which data was being generated across the various fields was more than the rate at which the data was being analyzed. Nonetheless, analysts felt that if well managed, such processed data could be a critical source of information for planning and decision making in many aspects.
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| According to social media experts, Twitter was a goldmine of data. It was one of the most popular micro blogging platforms that was used for research in opinion mining and sentiment analysis. In contrast with other social platforms, almost every user’s tweets were completely public and also specific.. |
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| Zeyuanyu Long and Di Dai, students from the University of California San Diego, conducted a systematic analysis on Fitbit related sentiments on Twitter and built multiple predictive models to identify the influence of different factors on the positive and negative sentiments... |
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| Jacky Arora and Sapna Bhoir, graduate students pursuing Master’s in Business Analytics at the Spears School of Business , Oklahoma State University, conducted the study titled ‘Categorization of Fitbit’s Customer Complaints on Twitter Using SAS® Enterprise Miner ’ in 2016. The primary objective of the study was to categorize Fitbit complaints on Twitter and recognize the major issues faced by customers such as whether it was related to activity tracking, design, tech specs, application interactivity, and so on.. |
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| According to a 2019 research report by Global Market Insights, Inc., the text analytics market size was set to exceed US$ 15 billion by 2026. The growth in the market was attributed to the rise in popularity of sentiment analysis among enterprises for identifying customer requirements and accordingly devising a product launch or marketing strategy .. |
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Exhibit I: List of Fitbit Products
Exhibit II: Number of Fitbit Users
Exhibit III: List of Companies Acquired by Fitbit
Exhibit IV: Consolidated statements of operations of Fitbit Inc.
Exhibit V: Steps of Text Mining
Exhibit VI: Sentiment Analysis Process
Exhibit VII: Text Mining-Process Flow
Exhibit VIII: Concept Link for “Charge HR”
Exhibit IX: Concept Link for “Fitbit Surge”
Exhibit X:
Text Analytics Market Size by Deployment Model 2015-2026
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