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Author: search.filters.author.Buranapanitkij, Jirawin

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    Item type:Publication,
    Mining social media crowd trends from thai text posts and comments
    (2019-01-01)
    Thanasopon, Bundit
    ;
    Buranapanitkij, Jirawin
    ;
    Netisopakul, Ponrudee
    Text mining from social media stream has attracted wide interests from both businesses and academics. Very large numbers of self-posts from crowd sources contains hidden trends, which can be valuable to a business enterprise. Crowd trend mining methods, together with an easily understood visual presentation, are thus in great demand. We present an approach to mining crowd trends from Thai text posts. A Thai language preprocessing module was necessary to transform continuous text into series of words. Our method could then mine general unforeseen crowd trends by using an automatic context extraction technique, tf-idf score and an aggregated opinion score calculated from automatically classified sentiments for each post or comment. The best sentiment classifier was chosen based on extensive experiments on the same data source. These scores were combined into one unified term popularity which was visualized as a word cloud on a web application. A case study used a popular Thai discussion website-Pantip. com-and achieved three interwoven desired goals: (1) extraction of general and unforeseen crowd trends from a Thai discussion website, (2) assigning unified popularity scores to each candidate term and (3) presenting those terms to end users in an easily comprehended form.
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    Extraction and evaluation of popular online trends: A case of Pantip.com
    (2017-07-01)
    Thanasopon, Bundit
    ;
    Sumret, Nattawut
    ;
    Buranapanitkij, Jirawin
    ;
    Netisopakul, Ponrudee
    Popular online trends detection from crowd becomes more and more essential for both trend followers and online sellers. However, huge amount of online posts, both text and images, has prevented trends detection to be manually processed. This article, focusing on text mining, aims to automatically extract popular online trends. A case study is performed on one of the most popular discussion forum websites in Thailand - i.e., Pantip.com. The approach involves employing several unsupervised text mining techniques, namely, TF-IDF and HTML scores, and supervised learning sentiment classification, to extract key topics and assess sentiment results, respectively. Also, we conducted an experiment on the performance of sentiment classification and found that support vector machine (SVM) outperformed other learning techniques. In addition, the authors developed a web- application incorporating the proposed approach. The application collects data from Pantip.com, identifies key concepts of posts and calculates the popularity of each key concept based on statistics and sentiment results.