TY  - JOUR
T1  - Automatic Rating for Services of Tourism Industry by using Opinion Mining
AU - Kumar Mishra, Rupesh AU - Bhradwaj, Meghana AU - Awasthi, A.K. AU - Berlanga Llavori, Rafael AU - Srinathan, Kannan 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 24
SP  - 7529
EP  - 7533
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.7529.7533
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.7529.7533
KW  - Opinion mining
KW  -tourism industry
KW  -lexical analysis
KW  -Naive Bayes
KW  -sentiwordnet
KW  -threshold
AB  - 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.
ER  -