Fast and Efficient Opinion Mining Technique for Online Reviews Using Ranking and Classification Algorithms


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Abstract


In recent years, the spectacular development of web technologies, lead to an enormous quantity of user generated information in online systems. This large amount of information on web platforms make them viable for use as data sources, in applications based on opinion mining and sentiment analysis. In this paper we propose a technique for extracting the opinions from the online user reviews. Initially the data extracted from the web document which is unstructured. This phase is used for formatting the data before sentiment analysis and mining. In the second phase Feature extraction and opinion extraction is done. The features like term frequency, Part of Speech (POS) are extracted from the words in the documents. After feature extraction, we extract useful information to rate them as positive, neutral, or negative. The positive and negative features are identified by extracting the associated modifiers and opinions. Finally the supervised learning algorithm decision tree classifier is implemented with the help of features extracted. In the final step ranking and classification will be done.
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Keywords


Opinion Mining; Web Document; Mining; Sentiment Analysis; Naïve Bayes

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