Text mining on social networks: Aviation industry

• Main Authors: WANG YU-YUN, 王允育
• Other Authors: MariaR.Lee
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/53820062647130130485

碩士 === 實踐大學 === 資訊科技與管理學系碩士班 === 104 === With the rise and development of the web 2.0 and social media websites, many enterprises have realized the importance of social media. More companies are making use of social media tools, such as Facebook, Twitter, and PTT, to interact with their customers to increase their commercial value. The commercial values include increasing client flow, increasing client loyalty and retention rate, increasing sales revenue, increasing client satisfaction, establishing brand image and reputation. In order to increase the competitiveness of the enterprise, as well as obtain an effective assessment of the market competition, enterprises should analyze the text contents that customers generate on their social media websites, and also mine the text content available on the websites of their competitors. The objective of these measures is to translate the social media data into understandable information to assist the decision-makers in the enterprises to rapidly understand the competitiveness of social media, as well as to make full use of the information for decision-making. In this research, three Taiwan airline companies, China Airlines, EVA Air, and TransAsia Airways, are studied to analyze the unstructured text content on their Facebook webpages, and the affection semantic sentences of the users in the aviation section of the Taiwan PTT BBS system, such as like comments and dislike comments, while customer emotions will be used to analyze the customer satisfaction of the airline services. In accordance with the professional airline research and certification organization, Skytrax Research, this research is classified into six major service indicators, flight punctuality, ground services, on-board catering, cabin, cleanliness, and in-flight entertainment. Research results show that the value of competitor analysis in social media and text mining are effective methods for the mass-extraction of commercial values from social media data. The results also provided practical suggestions for the corporate development of competition analysis strategies in social media.