At present, Big Data have been created lot of buzz in the technology world. Sentiment Analysis or opinion mining is one of the important applications of 'Big Data', where sentiment analysis is used for recognising voice or response of crowd for products, services. This concept describes the items in some detail and evaluate them as good/bad, preferred/not preferred. The results are very important for a company because customer feedback can yield extremely valuable insights about a company's customer. However, in a commercial website of product reviews, many customers can access to describe the items in some detail and evaluate them with different languages. Therefore, many companies will gather customer feedback in multiple languages. Definitely, feedback in multiple languages raises problems in analysing the material. As this, this paper proposes a solution to classify a product review dataset into two classes: positive and negative sentiments. The proposed methodology is called "Multilingual Sentiment Classification (MSC)". It consists of two main processing steps: lingual separation and sentiment classification. The first main processing step is to classify online product reviews into language classes. The second processing step is to classify each textual dataset into two classes: positive and negative sentiments. It is noted, we concentrate and experiment on bilingual texts (Thai and English).

Due to the flood of pornographic web sites on the internet, effective Web filtering systems are essential. Web filtering based on content has become one of the important techniques to handle and filter inappropriate information on the web. We examine two machine learning algorithms (support vector machines and Naive Bayes) for pornographic web filtering based on text content. We then focus initially on Thai-language and English-language web sites. In this paper, we aim to investigate whether machine learning algorithms are suitable for web sites classification. The empirical results show that the classifier based support vector machines are more effective for pornographic web filtering than Naive Bayes classifier after testing, especially an effectiveness for the over-blocking problem.