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Journal: International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) (Vol.6, No. 3)

Publication Date:

Authors : ; ;

Page : 301-306

Keywords : Keywords:social media; business intelligence; business indicators; machine learning; sentiment analysis;

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Abstract Social media and social networks underpin a revolution in communication between people, with the particular feature that much of that communication is open to all. This provides a massive pool of data that can be exploited by researchers for a wide variety of different applications. Data from Twitter is of particular interest in this sense, giving its large global usage levels, and the availability of APIs and other tools that enable easy access to the publically available stream of tweets. Owing to the wide public penetration of Twitter, many businesses make use of it to share their latest news, effectively using Twitter as a gateway to connect to end users, consumers and/or investors. In this paper, we focus on the potential for extracting information from Twitter that is relevant to the financial and competitiveness status of a business. We consider a collection of well-regarded Twitter accounts that are known for communicating recent business news, and we investigate automated analysis of the stream of tweets from these sources, with a view to learning business-relevant information about specific companies. A key aspect of our approach is the idea of extracting specific areas of business performance: we explore three such areas: productivity, competitiveness, and industrial risk. We propose a two-step model which first classifies a tweet into one of these areas, and then assigns a sentiment value (on a positive/negative scale). The resulting sentiment values across specific aspects represent novel business indicators that could add significant value to the toolset used by business analysts. Our experiments are based on a new manually pre-classified dataset (available from a URL provided). Broadly, we achieve promising results in both topic and sentiment classification, and we find that our system can provide valuable insight on current data. However more research is needed to be able to extract robust signals for industrial risk, and there seems to be considerable promise for further development.

Last modified: 2017-07-15 23:54:47