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Thai stock news classification based on price changes and sentiments
(2022-01-01)
Netisopakul, Ponrudee
;
Saewong, Woranun
This research investigates the daily stock news influences toward a company's stock price direction in the Stock Exchange of Thailand. First, machine learning's text classification methods, namely, naïve Bayes, decision tree, random forest, support vector machine, and the three-layer and the five-layer backpropagation neural networks, are applied to predict the stock price directions using stock news collected during the year 2018. Then, the stock news sentiment is incorporated to help improve the prediction accuracy. Last, a meaningful grouping of stock news is carried out to further improve the direction prediction. The testing dataset collected from January to March 2019 stock news are used for model evaluations. The best accuracy obtained from the baseline dataset using stock news only is 78.6%. When dataset is augmented with sentiments and grouped, the best accuracy increases to 90.6%.
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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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Business popularity analysis from twitter
(2018-01-01)
Yaisawas, Pajaree
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Lerdsri, Sukanlaya
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Thanasopon, Bundit
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Netisopakul, Ponrudee
Social media is increasingly utilized for sharing information of online products, from business owners to customers, as well as among customers themselves. In order to utilize these sharing information, this paper proposes and demonstrates the methodology for analyzing business brand popularity based on Twitter posts. The analysis can be visualized by implementing a web application that keeps track and analyzes Twitter posts mentioning about cosmetic and beauty product. Specifically, the application focuses on Twitter posts in Thai; and its key features are, (1) identifying brands being in trend, (2) analyzing and virtualizing statistics provided by Twitter, and (3) classifying Twitter posts’ sentiment into positive, negative and objective. The website provides useful insights to brand owners aiming at exploiting social media and to customers buying products from those brands.
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Hypothesis testing based on observation from Thai sentiment classification
(2017-06-01)
Netisopakul, Ponrudee
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Pasupa, Kitsuchart
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Lertsuksakda, Rathawut
This work focuses on error analyzes from the Support Vector Machine (SVM) classification on Thai children stories at a sentence level. The construction of the Sentiment Term Tagging System (STTS) program allows the researchers to make observations and hypothesize around the areas where most anomalies occur. Three hypotheses, based on terms sentiment chosen for SVM predictions, are evidently proved to hold. In addition, a number of ways to improve the Thai sentiment classification research are suggested, including considerations to add negation into the process, add weighing scheme for different part-of-speech, disambiguate word senses, and update the Thai sentiment resource.
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Sentiment analysis of Thai children stories
(2016-09-01)
Pasupa, Kitsuchart
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Netisopakul, Ponrudee
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Lertsuksakda, Rathawut
Sentiment Text Tagging System (STTS) with Thai sentiment resource has been developed and used to tag emotions directly to words and sentences in Thai children stories. The Thai sentiment resource, developed from SenticNet2 resource, groups emotions into four independent but concomitant dimensions: pleasantness, attention, sensitivity and aptitude. The measure of each dimension is called a sentic value of that dimension. Thai sentiment resource stores each word’s sentic value and polarity value, a value calculated from the sentic value, in the form of floating point number. The resource was constructed from bi-directional translation of 14,244 English terms in SenticNet2 into 16,584 Thai terms. The main purpose of this study was to implement a sentiment analysis of Thai children stories system with support vector machine using a set of proposed discriminating features for classifying emotions. It was found that the system can achieve 75.67 % of accuracy.
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Thai sentiment terms construction using the hourglass of emotions
(2014-01-01)
Lertsuksakda, Rathawut
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Netisopakul, Ponrudee
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Pasupa, Kitsuchart
Most of the current sentiment analysis techniques classifies emotions into two classes which are positive and negative. Some works classify them as positive, negative and objective (neutral). In fact, there are many kinds of emotions in human mind. Recently, psychological viewpoints have influenced most of the works in sentiment analysis. This psychology perspective was adopted to classify human emotions into a wider range, and in a more accurate manner. This paper reviews the adopted computational representation of emotions the so-called Hourglass of Emotion. This paper also proposes a construction of Thai sentiment resource based on such representation for Thai sentiment term tagging. A preliminary sentiment text tagging result shows that the resource as an ontology can be successfully used to tag sentiment text in Thai children stories. © 2013 IEEE.

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