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In the last decade, the amount of social media usage has rapidly increased exponentially in Thailand. A huge amount of Thai online reviews and comments are available on social network every second. Because of this fact, comment analysis, also called sentiment analysis, has then become an essential task to analyze people’s emotions, opinion, attitudes and sentiments from the amount of these online posts. This paper proposed the technique for analyzing Thai customers’ comments or opinions about the products and services by counting the polarity words of the product and service domains. To demonstrate the proposed technique, experimental studies on analyzing Thai customers’ comments in the social media are presented in this paper. The comments are classified into neutral, positive or negative. The proposed technique benefits the business domain in guiding product improvement and quality of service. Hence, this paper also benefits the end-users in making a smart decision.

This paper proposes a non-segmented document clustering method using self-organizing map (SOM) and frequent max substring technique to improve the efficiency of information retrieval. SOM has been widely used for document clustering and is successful in many applications. However, when applying to non-segmented document, the challenge is to identify any interesting pattern efficiently. There are two main phases in the propose method: preprocessing phase and clustering phase. In the preprocessing phase, the frequent max substring technique is first applied to discover the patterns of interest called Frequent Max substrings that are long and frequent substrings, rather than individual words from the non-segmented texts. These discovered patterns are then used as indexing terms. The indexing terms together with their number of occurrences form a document vector. In the clustering phase, SOM is used to generate the document cluster map by using the feature vector of Frequent Max substrings. To demonstrate the proposed technique, experimental studies and comparison results on clustering the Thai text documents, which consist of non-segmented texts, are presented in this paper. The results show that the proposed technique can be used for Thai texts. The document cluster map generated with the method can be used to find the relevant documents more efficiently

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