Journal ArticleDOI
Social media data analytics for business decision making system to competitive analysis
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TLDR
In this article , a business decision making system (BDMS) is proposed to develop business using social media data analytics, which provides a clear understanding of the key principles, issues and functionality, and big social data developments.Abstract:
For the past few years, business intelligence has been a major field that uses data analysis to produce key information as part of business decision-making. Data collected from social media sites and blogs are analyzed to make business decisions, a process called social media analytics (SMA). This method, which goes beyond ordinary monitoring or a basic analysis of retweets, develops an in-depth insight into the social consumer. After reading the whole report, add the pertinent figures to the table. Add pertinent data from the Brand24 report to the table. During a social media audit, any followers, impressions, engagement, copy/traffic, and brand mentions are key parameters to analyze. For companies and research institutions, the great interest is to analyse and gain knowledge from user-produced data. These data contain useful knowledge, including customer perceptions feedback and product/service suggestions. Due to content saturation, social media's true meaning regarding business data is hardly ever found. Therefore, in this paper, the business decision making system (BDMS) has been proposed to develop business using social media data analytics. BDMS provides a clear understanding of the key principles, issues and functionality, and big social data developments. Besides, BDMS concentrates on marketing and describes an operational approach for obtaining valuable information from social data. BDMS performs a short and precise description of current use scenarios from the evidence, as per the help of decisions and investment opportunities companies get when using social data analytics. The experimental result shows that BDMS achieves the highest competitive results. With greater accuracy, system dependability, F-1 measurement, and deviation rate of 85.5%, the BDMS system guarantees 93.7%, 86.8%, and 7.0%. read more
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Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network
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TL;DR: In this article , a semantic-based taxonomy of risks is devised to discover and analyse associated events from data streams, with a view to: (i) read and process, in real-time, published texts (ii) classify each text into one representative real-world category (iii) assign a citizen satisfaction value to each event.
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Linking knowledge acquisition, knowledge dissemination, and manufacturing SMEs’ sustainable performance: the mediating role of knowledge application
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References
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Journal ArticleDOI
Coupling mobile phone and social media data: a new approach to understanding urban functions and diurnal patterns
Wei Tu,Jinzhou Cao,Yang Yue,Shih-Lung Shaw,Meng Zhou,Zhensheng Wang,Xiaomeng Chang,Yang Xu,Qingquan Li +8 more
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