Sentiment analysis on university satisfaction in social media

The feedback of college students by surveys is rather difficult. Twitter provides a large data set for student sentiment analysis. In this article, both lexical and classification based approaches have been tested. Six different classification algorithms, Naive Bayes, SVM, Bayesian Network, C4.5 Decision Tree, Bagging and Random Forest, were applied to the classification based approach and the results were compared with each other.. 10 fold cross validation was used during the test phase. Similar results have been observed for Twitter sentiment analysis when looking at the results. Within the scope of this study, Based on a Lexicon based approach the happiness rates were compared for six universities, three of which are state universities and three of which are foundation universities in Istanbul. When the results are compared, it gives unhappy results for a university while giving the happy results for five universities. This study for university sentiment classification will give more accurate results with more data. In addition, it is thought that more accurate results will be obtained by increasing the number of words in the dictionary. A work to be done in this regard will increase the accuracy of the current work.