Abstract:The most important element in analyzing sentiment in text is to assign polarity to the opinion words. Polarity means the positive, negative or neutral state of the opinion words. They are many methods or ways in determining the polarity of an opinion words. One of the methods is using lexicon-based method. Lexicons are digital library of opinion words together with the polarity of the words. Basically, there are 3 methods in developing lexicon-based approach which is manual, dictionary-based and corpus-based. For Malay language there is no available sentiment lexicon and also very limited sources. Thus, in this study we present the automation lexicon generation for Malay language using the dictionary approach. The detail description of the automation lexicon generation for Malay language is discussed in this study.

Growing of social media usage present a new set of opportunities and challenges in the way of information is retrieved and searched. Opinions on social media has become an important factor in influencing people choices on purchasing a product and service. Hence, sentiment analysis has become the most crucial tool in tracking people feedbacks on products and services. For Malay language there is limited sources available for this language. Thus, in this paper we present the method of extracting opinion on online Malay text. The traditional method using POS extraction is not adequate. Thus, rule based method is integrated with POS extraction method to improve opinion words extraction. Most of the existing tools are able to retrieve opinion at sentence and document level. More detail analysis is acquired to have detail information and summarization of a product. This is where feature level sentiment analysis is needed. The process of identifying opinion of a particular feature in a sentence, can be quite tedious and troublesome. This is because opinion of the feature can be hidden and scattered in the sentence. Therefore, opinion mapping is employed for opinion extraction at feature level in this paper. A set of tweets from telecommunication domain is used to evaluate the proposed framework. From the experiment, the accuracy of the extraction performed is 88%. The detail description of the feature level opinion extraction steps is discussed in this paper.