Opinion Mining from Customer Feedback Data on the Web

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As people leave on the Web their opinions on products and services they have used, it has become important to develop methods of (semi) automatically classifying and gauging them. The task of analyzing such data, collectively called customer feedback data, is known as opinion mining. Opinion mining consists of several steps, and multiple techniques have been proposed for each step. The World Wide Web is growing at an alarming rate not only in size but also in the types of services and contents provided. Individual users are participating more actively and are generating vast amount of new data. These new Web contents include customer reviews and blogs that express opinions on products and services, which are collectively referred to as customer feedback data on the Web. As customer feedback on the Web influences other customer’s decisions, these feedbacks have become an important source of information for businesses to take into account when developing marketing and product development. In this project, a software system for opinion mining is developed.

Conjunction Method

In this method, we try to develop linguistic resources for opinion mining. Conjunction Method takes in to consideration analysis of textual corpora, which tries to correlates linguistic features with semantic orientation. System uses this information to extract conjunctions of adjectives from the corpus along with relevant morphological relations. Information from different conjunctions is combined by a log linear regression model to determine if each of the two adjectives is of the same or different orientation. 

PMI Method

Measure of association used in information theory and statistics is called PMI. Advantages of PMI is that it can easily be scaled up to very large corpora, where it can achieve significantly higher accuracy.

WordNet Exploring Method

To predict the semantic orientation of adjectives, adjective synonym and antonym set in WordNet is used. In WordNet, adjectives are organized into bipolar clusters and share the same orientation of their synonyms and opposite orientation of their antonyms. To assign orientation of an adjective, the synset of the given adjective and the antonym set are searched. If a synonym/antonym has known orientation, then the orientation of the given adjective could be set correspondingly. As the synset of an adjective always contains a sense that links it to the head synset, the search range is rather large. Given enough seed adjectives with known orientations , the orientations of all the adjective words can be predicted

Gloss Classification Method

To develop linguistic resources by classifying term glosses. They assume that terms with similar orientation have similar glosses and terms without orientation have non oriented glosses



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