Context-Sensitive Opinion Mining using Polarity Patterns

Abstract: The growing of Web 2.0 has led to huge information is available. The analysis of this information can be very useful in various fields. In this regards, opinion mining and sentiment analysis are one of the most interesting task that many researchers have paid attention for two last decades. However, this task involves to some challenges that a very important challenge is the different polarity of words in various domain and context. Word polarity is an important feature in the determination of review polarity through sentiment analysis. Existing studies have proposed n-gram technique as a solution which allows the matching of the selected words to the lexicon. However, identification of word polarity using the standard n-gram method poses limitation as it ignores the word placement and its effect according to the contextual domain. Therefore, this study proposes a linguistic-based model to extract the word adjacency patterns to determine the review polarity. The results reflect the superiority of the proposed model compared to other benchmarking approaches.

Saeedeh Sadat Sadidpour, Hossein Shirazi, Nurfadhlina Mohd Sharef, Behrouz Minaei-Bidgoli and Mohammad Ebrahim Sanjaghi. “Context-Sensitive Opinion Mining using Polarity Patterns”. International Journal of Advanced Computer Science and Applications (IJACSA) 7.9 (2016). http://dx.doi.org/10.14569/IJACSA.2016.070920

@article{Sadidpour2016,
title = {Context-Sensitive Opinion Mining using Polarity Patterns},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070920},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070920},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {9},
author = {Saeedeh Sadat Sadidpour and Hossein Shirazi and Nurfadhlina Mohd Sharef and Behrouz Minaei-Bidgoli and Mohammad Ebrahim Sanjaghi}
}