Abstract
Opinion Mining (OM) is concerned with the retrieval or extraction of information and discovery of knowledge from the text, collected through opinions of people e.g. about different products services, using Natural Language Processing and Data Mining techniques. Researchers are focusing on different areas of opinion mining or sentiment analysis (both terms will be used interchangeably in this review). One very popular area nowadays is aspect based opinion summarization. Researchers have introduced new methods/techniques or used already existing ones to achieve this goal. This paper provides a survey of such attempts highlighting the use of different techniques including Natural Language Processing (NLP) techniques, feature discovery mining techniques for aspect/feature identification, learning and lexicon based methods for sentiment prediction and different ways of summaries that are commonly used to generate an aspect based summary. A frame work containing multiple approaches for opinion summarization is presented. Three common steps (i.e. aspect/feature identification, sentiment prediction and aspect/feature based summary) that are normally taken for aspect based opinion summarization are discussed in detail. An ontology is designed that provides a quick overview of all these phases. The paper is concluded by highlighting the current limitations faced by the researchers in this discipline and thus providing indications for future research.
Neelam Mukhtar, Mohammad Abid Khan. (2014) Aspect based Opinion Mining, a review, Conference on Language and Technology 2014.
-
Viewed
1508 -
Downloads
0
Publisher
Center for Language Engineering
Country
Pakistan
City
Karachi
From
13-11-2014
To
15-11-2014