Lexical Based Method for Opinion Detection on Tripadvisor Collection

The massive development of online social networks
allows users to post and share their opinions on various topics.
With this huge volume of opinion, it is interesting to extract and
interpret these information for different domains, e.g., product and
service benchmarking, politic, system of recommendation. This is
why opinion detection is one of the most important research tasks.
It consists on differentiating between opinion data and factual data.
The difficulty of this task is to determine an approach which returns
opinionated document. Generally, there are two approaches used
for opinion detection i.e. Lexical based approaches and Machine
Learning based approaches. In Lexical based approaches, a dictionary
of sentimental words is used, words are associated with weights. The
opinion score of document is derived by the occurrence of words from
this dictionary. In Machine learning approaches, usually a classifier
is trained using a set of annotated document containing sentiment,
and features such as n-grams of words, part-of-speech tags, and
logical forms. Majority of these works are based on documents text
to determine opinion score but dont take into account if these texts
are really correct. Thus, it is interesting to exploit other information
to improve opinion detection. In our work, we will develop a new
way to consider the opinion score. We introduce the notion of
trust score. We determine opinionated documents but also if these
opinions are really trustable information in relation with topics. For
that we use lexical SentiWordNet to calculate opinion and trust
scores, we compute different features about users like (numbers of
their comments, numbers of their useful comments, Average useful
review). After that, we combine opinion score and trust score to
obtain a final score. We applied our method to detect trust opinions in
TRIPADVISOR collection. Our experimental results report that the
combination between opinion score and trust score improves opinion
detection.




References:
[1] Haji Binali, Vidyasagar Potdar, Chen Wu, A State Of The Art Opinion
Mining And Its Application Domains, 2014.
[2] Marco Guerini, Marco Turchi, Lorenzo Gatti, Sentiment Analysis: How
to Derive Prior Polarities from SentiWordNet, 2013.
[3] Julia Kreutzer, Neele Witte, Opinion Mining Using SentiWordNet, 2014.
[4] Bruno Ohana, Brendan Tierney, Sentiment Classification of Reviews
Using SentiWordNet, 2009.
[5] Oscar Romero Llombart, Using Machine Learning Techniques for
Sentiment Analysis, 2014.
[6] Guido Boella and Leonardo Lesmo, Automatic Refinement of Linguistic
Rules for Tagging, 2012.
[7] Walaa Medhat Ahmed Hassan Hoda Korash, Sentiment analysis
algorithms and applications: A survey, 2014.
[8] Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani,
SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment
Analysis and Opinion Mining.
[9] Bo Pang and Lillian Lee, Opinion mining and sentiment analysis, 2008.
[10] Jayashri Khairnar, Mayura Kinikar, Machine Learning Algorithms for
Opinion Mining and Sentiment Classification, 2013.
[11] Hongning Wang, Yue Lu, Chengxiang Zha, Latent Aspect Rating
Analysis on Review Text Data: A Rating Regression Approach.
[12] Dietmar Grbner, Markus Zanker, Grnther Flied, Matthias Fuchs,
Classification of customer reviews based on Sentiment analysis.
[13] Walter Kasper, Mihaela Vela, Sentiment Analysis for Hotel Reviews.