Authors : Rupesh Kumar Mishra, Meghana Bhradwaj, A.K. Awasthi, Rafael Berlanga Llavori and Kannan Srinathan
Abstract: In this study, we have proposed a platform for extracting and summarizing users opinions about the services offered by the tourism industry. Perspectives extracted from the public generated content regarding aspects specific to services provided at various tourist spots and adventure spots are useful to both people who want to visit that place as well as the tourism industry to help them in improving their services. Here, using Naive Bayes classifier is applied to classify the data of a tourism industry based upon either positive and negative Tweets and posts on the given social media. In that approach the interconnection between of the post and public opinion has successfully managed by using the score of that post has to exist in the opinion or not. Also defined the threshold for the given Tweets or post on social media and we cannot take those post or Tweets which of score less than the measure threshold. The proposed system uses a hybrid approach mixing lexical and supervised learning methods. Three types of data we have taken for the experiment for this problem are first all the online news related to tourism data and non tourism data and its public opinion, second one the facebook post of public opinion and the third one is taken a Twitter data and TripAdvisor datasets.
Rupesh Kumar Mishra, Meghana Bhradwaj, A.K. Awasthi, Rafael Berlanga Llavori and Kannan Srinathan, 2017. Automatic Rating for Services of Tourism Industry by using Opinion Mining. Journal of Engineering and Applied Sciences, 12: 7529-7533.