TY - EJOU AU - Mehmood, Shahid AU - Ahmad, Imran AU - Khan, Muhammad Adnan AU - Khan, Faheem AU - Whangbo, T. TI - Sentiment Analysis in Social Media for Competitive Environment Using Content Analysis T2 - Computers, Materials \& Continua PY - 2022 VL - 71 IS - 3 SN - 1546-2226 AB - Education sector has witnessed several changes in the recent past. These changes have forced private universities into fierce competition with each other to get more students enrolled. This competition has resulted in the adoption of marketing practices by private universities similar to commercial brands. To get competitive gain, universities must observe and examine the students’ feedback on their own social media sites along with the social media sites of their competitors. This study presents a novel framework which integrates numerous analytical approaches including statistical analysis, sentiment analysis, and text mining to accomplish a competitive analysis of social media sites of the universities. These techniques enable local universities to utilize social media for the identification of the most-discussed topics by students as well as based on the most unfavorable comments received, major areas for improvement. A comprehensive case study was conducted utilizing the proposed framework for competitive analysis of few top ranked international universities as well as local private universities in Lahore Pakistan. Experimental results show that diversity of shared content, frequency of posts, and schedule of updates, are the key areas for improvement for the local universities. Based on the competitive intelligence gained several recommendations are included in this paper that would enable local universities generally and Riphah international university (RIU) Lahore specifically to promote their brand and increase their attractiveness for potential students using social media and launch successful marketing campaigns targeting a large number of audiences at significantly reduced cost resulting in an increased number of enrolments. KW - Social media; higher education; sentiment analysis; content analysis; competitive analysis; text mining DO - 10.32604/cmc.2022.023785