Tourism Companies Assessment via Social Media Using Sentiment Analysis

Abstract

In recent years, social media has been increasing widely and obviously as a media for users expressing their emotions and feelings through thousands of posts and comments related to tourism companies. As a consequence, it became difficult for tourists to read all the comments to determine whether these opinions are positive or negative to assess the success of a tourism company. In this paper, a modest model is proposed to assess e-tourism companies using Iraqi dialect reviews collected from Facebook. The reviews are analyzed using text mining techniques for sentiment classification. The generated sentiment words are classified into positive, negative and neutral comments by utilizing Rough Set Theory, Naïve Bayes and K-Nearest Neighbor methods. After experimental results, it was determined that out of 71 tested Iraqi tourism companies, 28% from these companies have very good assessment, 26% from these companies have good assessment, 31% from these companies have medium assessment, 4% from these companies have acceptance assessment and 11% from these companies have bad assessment. These results helped the companies to improve their work and programs responding sufficiently and quickly to customer demands.

قدير النمو السياحي في مواقع الوجهات السياحية: معادلة رياضية ونموذج الاتصال من خلال تطبيقات الهاتف المحمول

“…The comments were classified by three methods KNN, Naïve Bayes and Rough Set theory. The outcome of the experiments revealed to the e-tourism companies for their future predictions 23 .…”

Healthcare assessment for beauty centers using hybrid sentiment analysis

“…Negative class gives advice that something is bad, so the decision will be leave it. While positive class gives advice that something is good, so the decision will be use it [3]- [5].…”