Lalittrojwong, Pattarachai

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Extracting product features and opinions from product reviews using dependency analysis
(2010-11-29)
Somprasertsri, Gamgarn
;
Lalitrojwong, Pattarachai
In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. Online product reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The task of product feature and opinion is to find product features that customers refer to their topic reviews. It would be useful to characterize the opinions about product. In this paper, we propose an approach to extract product features and to identify the opinions associated with these features from reviews through syntactic information based on dependency analysis. ©2010 IEEE.
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OntoPop: An ontology population system for the semantic web
(2012-01-01)
Thongkrau, Theerayut
;
Lalitrojwong, Pattarachai
The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. How ever, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syn tactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.9%, and executes time of 5.46 second/instance. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.
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Classifying instances into lexical ontology concepts using latent semantic analysis
(2010-05-28)
Thongkrau, Theerayut
;
Lalitrojwong, Pattarachai
A lexical ontology is useful as the basic knowledge base in artificial intelligence and computational linguistics application. However, it is insufficient to recognize only existing instances for each concept. Adding new instances into the lexical ontology will expand knowledge in the system. In this paper, we propose an efficient unsupervised instance population system that classifies new instances into a corresponding lexical ontology concept. Compared to previous related works, it does not require manual preprocessing to prepare training data. In terms of processing time, it does not need to search for many concepts in the lexical ontology. Furthermore, it is able to handle an unlimited number of ontological concepts in any domain. Our system employs latent semantic analysis together with context voting to find the appropriate concept of the instance. The experiments demonstrate that this approach compared to similarity approach yields higher accuracy for instance classification. In sum, the system achieves higher accuracy when the lexical ontology contains a lot of concepts, which generally occurs in practical problems. ©2010 IEEE.
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Mining feature-opinion in online customer reviews for opinion summarization
(2010-06-18)
Somprasertsri, Gamgarn
;
Lalitrojwong, Pattarachai
Online customer reviews is considered as a significant informative resource which is useful for both potential customers and product manufacturers. In web pages, the reviews are written in natural language and are unstructured-free-texts scheme. The task of manually scanning through large amounts of review one by one is computational burden and is not practically implemented with respect to businesses and customer perspectives. Therefore it is more efficient to automatically process the various reviews and provide the necessary information in a suitable form. The high-level problem of opinion summarization addresses how to determine the sentiment, attitude or opinion that an author expressed in natural language text with respect to a certain feature. In this paper, we dedicate our work to the main subtask of opinion summarization. The task of product feature and opinion extraction is critical to opinion summarization, because its effectiveness significantly affects the performance of opinion orientation identification. It is important to properly identify the semantic relationships between product features and opinions. We proposed an approach for mining product feature and opinion based on the consideration of syntactic information and semantic information. By applying dependency relations and ontological knowledge with probabilistic based model, the result of our experiments shows that our approach is more flexible and effective. © J.UCS.

Lalittrojwong, Pattarachai

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