The higher education opportunities have increased significantly over the past decade in Sri Lanka. Today's younger generation is keen to study and most of them opt for higher education. Choosing the right course at the right private institute is most challenging choice since there are so many options available. In order to find the right institute, students have to surf internet for the reviews and find user comments of particular institution from social network sites like Facebook, Twitter, Google plus and etc. This takes lot of time for reading the comments to understand whether that ratings are good or not on the particular institution. The key information a student wants to get from the review is: whether that institute is good, and what aspects received positive or negative opinions. This task is quite challenging because it is difficult for a human being to extract statistical aspect sentiment information from a massive set of online reviews. As a solution for this problem higher institution aspect based evaluation system which evaluates the institution by considering the reviews given by reviewers is suggested by this project. This system implementation is based on natural language processing. The outcome of this research project, is a system which retrieves review data from the social media networks and gives a rating to an institution by analyzing the sentiment value of the reviews and the features evaluated in them. Data gathering and analysis process of this project is made automated as possible and this can be accessed from anywhere, as the client application is developed as a web application.
[1]
Yu Zhang,et al.
Extracting implicit features in online customer reviews for opinion mining
,
2013,
WWW '13 Companion.
[2]
Frank Rennie.
Using Social Media in Higher Education
,
2014
.
[3]
Yubo Chen,et al.
Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix
,
2004,
Manag. Sci..
[4]
Srikala.
Classification and Clustering in Data Mining
,
2017
.
[5]
Efthymios Constantinides,et al.
Potential of the social media as instruments of higher education marketing: Guidelines for a social media marketing strategy for the University of Twente
,
2010
.
[6]
Robert F. Ling,et al.
Classification and Clustering.
,
1979
.