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Detecting Thai messages leading to deception on facebook
(2016-01-01)
Songram, Panida
;
Choompol, Atchara
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Thipsanthia, Paitoon
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Boonjing, Veera
Social network has become a very popular communication for Thai people, especially Facebook. Unfortunately, this popularity also attracts deceiver spreading malicious messages to other users. Some messages lead to deception. This paper studies Thai messages posted on Facebook that lead to deception. We try to investigate different approaches to detect deceptive messages and find dominant words. To detect deceptive messages, the dataset is retrieved from Facebook pages. Next, content-based and context-based features are extracted from the dataset. Two algorithms, i.e. SVM and KNN, are applied to perform a prediction. We construct the experiments to investigate context-based and content-based features for detecting deceptive messages. The experimental results show that the context-based features gives the best performance and the F-measure for predicting deceptive messages achieves 99% when using SVM classifier. In addition, dominant words in deceptive messages and truthful messages are reported in our work.
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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.
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A maximum entropy model for product feature extraction in online customer reviews
(2008-01-01)
Somprasertsri, Gamgarn
;
Lalitrojwong, Pattarachai
Product feature extraction is an important task of review mining and summarization. The task of product feature extraction is to find product features that customers refer to in their topic reviews. It would be useful to characterize the opinions which they review or express about the products. In this paper, we propose an approach to product feature extraction using a maximum entropy model. Maximum entropy is a probability distribution estimation technique. It is widely used for classification problems in natural language processing, such as question answering, information extraction, and part-of-speech tagging. The underlying principle of maximum entropy is that without external knowledge, one should prefer distributions that are uniform. Using a maximum entropy approach, at first we extract features from the corpus, train maximum entropy model with an annotated corpus, and then use it with additional product feature discovery to extract product features from customer reviews. Our experimental results show that this approach can work effectively for product feature extraction with 71.88% precision and 75.23% recall. © 2008 IEEE.
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Dimensionality reduction of features for text categorization
(2007-12-01)
Jitpakdee, Parisut
;
Kreesuradej, Worapoj
This paper proposes a new technique for dimensionality reduction of features for text categorization. Unlike conventional method, our phrase features are generated based on word sequences of different length (Multigrams) from phrases extracted from whole documents. Then, we utilize Odds ratio (OR) to perform phase feature selection. From preliminary experiments, the proposed techniques show better performance than that of conventional methods.
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Text processing simplified ARTMAP neural network
(2005-02-01)
Kreesuradej, Worapoj
;
Kunasit, Puangpaka
This paper proposes text processing simplified ARTMAP neural network. The algorithm works directly on textual information without transforming to numerical value. The input layer of the neural network can directly receive a qualitative value without mapping the qualitative value into numerical value. Then, based on simplified fuzzy ARTMAP neural network and the concept of similarity measure for symbolic objects, the proposed neural network can assigns class labels to the objects correctly.

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