Analyzing social media content to gain competitive intelligence
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2017
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eng
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110 leaves
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b199210
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ผลงานนี้เผยแพร่ภายใต้ สัญญาอนุญาตครีเอทีฟคอมมอนส์แบบ แสดงที่มา-ไม่ใช้เพื่อการค้า-ไม่ดัดแปลง 4.0 (CC BY-NC-ND 4.0)
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National Institute of Development Administration. Library and Information Center
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Jitrlada Rojratanavijit (2017). Analyzing social media content to gain competitive intelligence. Retrieved from: https://repository.nida.ac.th/handle/662723737/6276.
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Analyzing social media content to gain competitive intelligence
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Abstract
The emergence of social media in Thailand has given millions of users a platform to express and share their opinions about products and services, among other subjects, and so Twitter is considered to be a rich source of information for companies to understand their customers by extracting and analyzing sentiment from Tweets. The main goal was to investigate the possible uses of Twitter information for businesses in Thailand to take advantage of and to solve any associated limitations caused by the semantics of the Thai language. Hence, social media content, specifically Tweets were utilized to generate Competitive Intelligence (CI). A new method for Twitter sentiment analysis called ASTS was proposed by using both supervised learning and lexiconbased techniques. Experiments were conducted using Tweets on three mobile network operator companies: AIS, DTAC, and TRUEMOVEH obtained using the Twitter search API focused on Tweets in Thai. A total of 72,661 were collected over a period of six months (from October 1, 2014 to March 31, 2015). ASTS consists of three modules: (1) data collection, (2) data pre-processing, and (3) classification and evaluation. A collection program was developed to search for keywords in the Twitter feed using the Twitter Search API and setting the language parameter “lang=th” and excluding reTweets. The process for the data pre-processing module was divided into three steps: (1) Text extraction from the Tweets, (2) Text preprocessing, and (3) Thai word segmentation. For the classification and evaluation module, the main intention was to identify opinion polarity, positive, negative, and neutral. The classification process was divided into two sub-modules: opinion filtering using supervised learning techniques and opinion polarity identification using lexiconbased techniques. Experimental results showed that the proposed method overcomes previous limitations from other studies and was very effective in most cases. The average accuracy is 84.80% with 82.42% precision, 83.88% recall and 82.97% Fmeasure. In particular, this clearly shows that opinion filtering helped to analyze Tweets more accurately. A case study approach for CI in social media aptly demonstrated the use of ASTS. Out of a total of 20,269 Tweets, 9,631 mentioned AIS (47.52%), 7,099 mentioned DTAC (35.02%), and 3,539 mentioned TRUEMOVEH (17.46%). The sentiment scores from the analysis results of using ASTS showed the overall customer sentiment for the companies. The sentiment score for TRUEMOVEH (-0.27) was slightly better than AIS (-0.38) and DTAC (-0.45). Benchmarking against competitors is essential information for CI. Strength and weakness analysis on the companies was derived using radar charts of the benchmarking of sentiment scores on the top five keyword mentions (net, wifi, promotion, switching, and employee). Furthermore, examples of using CI in terms of monitoring, opportunity events, and early warning alerts were presented. Opportunity events can be advantageous in response to negative sentiment Tweets on competing companies and can help a company to entice customers away from competing companies. Early warning alerts are based on negative sentiment Tweets on a company from which it should quickly identify customer dissatisfaction and then correct the associated problem. The results of this study show the usefulness of the proposed method for theoretical reference and as a practical guide. The findings from this analysis prove that CI extracted from social media content can help businesses to understand their customers’ opinions and compare them with those of their competitors. As a result, this research illustrates that CI from analyzing social media content has great potential to produce useful information, actionable knowledge, and critical insights for companies to enhance competitiveness and solve business problems.
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Thesis (Ph.D. (Applied Statistics))--National Institute of Development Administration, 2017