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Articles by Phayung Meesad
Total Records ( 2 ) for Phayung Meesad
  Preedawon Kadmateekarun , Phayung Meesad and Sumitra Nuanmeesri
  The cosmetic industry has been in a very high marketing competition by advertising through various media to promote sales and build up images of the products. In addition, consumers can access information through a search engine finding that there are up to 45.3 billion video clips and movies of beauty online in social network such as YouTube. Consumers are able to share messages, voices, pictures and video clips and movies through these media swiftly. There are both content and opinions indicating “like” (Positive) or “dislike” (Negative) on the products. These opinions can be brought to conduct the sentiment analysis on the products. This research focuses on automatic sentiment analysis. Therefore, this study aims at the automatic sentiment analysis which is a part of natural language processing of the cosmetic product, lipstick. The research methodology consists of the following steps. Firstly on the collection of data in Thai language such as criticisms on lipstick from YouTube to separate audio signals; secondly, the audio tracks conversion into texts to cut up into words in transcription. Next, the machine learning technique consisting of Naive Bayes (NB) and Support Vector Machine (SVM) to be used for the analysis of the consumer’s sentiment towards the lipstick. Finally, The measurement for the efficiency and the comparative study to find result from those different techniques. As a result, the Support Vector Machine (SVM) Technique is found to offer the best result with the accuracy value at 85.17%.
  Wipawan Buathong and Phayung Meesad
  This study proposes an alternative feature selection technique for dimensionality reduction namely Double Linear Support Vector Machine or “DLSVM” Weight. The efficiency of DLSVM was measured based on four performance evaluation criteria (i.e., accuracy, F-measure, precision and recall). The efficiency of well recognised feature selection techniques was also measured for comparative purposes. The Support Vector Machine (SVM), a prominent classifier was also used with DLSVM and these feature selection techniques. The Leukemia dataset from the University of California Irvine (UCI) machine learning repository was used for the experiments. Downsized data dimensions were classified into 60, 50, 40, 30, 20 and 10, respectively. The experimental results showed that the DLSVM was much more efficient than other feature selection techniques at almost all of the data dimensions. Particularly, all performance evaluation criteria of DLSVM could reach 100% when original data dimensions were downsized from 5,147-60, 50 and 40.
 
 
 
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