Abstract
with the increase of opinionated reviews on the web, automatically analyzing and extracting knowledge from those reviews is very important. However, it is a challenging task to be done manually. Opinion mining is a text mining discipline that automatically performs such a task. Most researches done in this field were focused on English texts with very limited researches on Arabic language. This scarcity is because there are a lot of obstacles in Arabic. The aim of this paper is to develop a novel semantic feature-based opinion mining framework for Arabic reviews. This framework utilizes the semantic of ontologies and lexicons in the identification of opinion features and their polarity. Experiments showed that the proposed framework achieved a good level of performance compared with manually collected test data.
Highlights
As a result of dramatically increase of using the internet in the recent years; huge information of people opinions was produced on the web, people can post their views using Internet forums, discussion groups, product reviews and blogs
A new methodology is proposed for feature-based Arabic opinion Mining
This approach is going through five different stages: Ontology and lexicon Development, Semantic Feature Identification, Polarity Identification, Feature Polarity Identification and Opinion Mining
Summary
As a result of dramatically increase of using the internet in the recent years; huge information of people opinions was produced on the web, people can post their views using Internet forums, discussion groups, product reviews and blogs. Analyzing this information manually is time consuming and maybe impossible mission. Basic task in opinion mining is to determine the subjectivity, polarity (positive or negative) of a piece of text in other words: What is the opinion of the writer. Opinion mining has a wide range of applications from different domains such as commercial, governmental, political, educational and others [2]
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