Due to the development of e-commerce and web technology, most of online Merchant sites are able to write comments about purchasing products for customer. Customer reviews expressed opinion about products or services which are collectively referred to as customer feedback data. Opinion extraction about products from customer reviews is becoming an interesting area of research and it is motivated to develop an automatic opinion mining application for users. Therefore, efficient method and techniques are needed to extract opinions from reviews. In this paper, we proposed a novel idea to find opinion words or phrases for each feature from customer reviews in an efficient way. Our focus in this paper is to get the patterns of opinion words/phrases about the feature of product from the review text through adjective, adverb, verb, and noun. The extracted features and opinions are useful for generating a meaningful summary that can provide significant informative resource to help the user as well as merchants to track the most suitable choice of product.
User-generated texts such as reviews, discussions or comments are valuable indicators of users' preferences. Apart from binary classification (positive or negative) of the reviews, some researchers calculated polarity scores that give a very concise summary and provide more information of the reviews. In this paper, a system for assigning polarity scores to Facebook Myanmar movie comments is proposed. Myanmar is a language with underdeveloped electric resources. As this is pioneering work for this combination of language and sentiment analysis, the polarity scores of each positive and negative word in the movie domain-specific polarity lexicon is calculated. And then the polarity scores to each comment of the plain text movie corpus are assigned. The proposed system achieves 89% and 85% accuracy on positive and negative opinion words respectively in the evaluation of polarity score lexicon. We also make the comment polarity for 3-class evaluation and 5-class evaluation based on the scores of comments.
General TermsSentiment analysis
This paper proposed a unified approach for Myanmar Word analysis using Finite State Automata (FSA), Rule Based Heuristic Approach and Statistical Approach. Myanmar has no inter-word space and it make the tokenizing task difficulties. Therefore, to recognize the word, we implement with FSA. Segmentation is a major problem because of no delimiter. If there were errors in segmentation, this will cause subsequence failure in further NLP processes. Segmentation is also an essential preprocessing task for Natural Language Processing, such as Machine Translation, Information Retrieval etc. In this system, the Rule Based Heuristic Approach and Statistical Approach are used with corpus based dictionary. Evaluation results showed that the method is very effective for the Myanmar language.
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