Opinion Mining using Lexicon Matching Based on Hidden Markov Model
Illustrations and Findings
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A large number of products are available today in various websites for trading. In order to know about the product, the seller or the manufacturer often asks their customers to share their opinions and their experiences on the products which they purchased. Unfortunately, this is a very cumbersome task to go through all review comments and to decide whether the product is up to the satisfaction level of customer or not. The main issue with these review comments is to manage all those comments and make a meaningful summarized form of review whether it represents a positive sense or feedback abo...
A large number of products are available today in various websites for trading. In order to know about the product, the seller or the manufacturer often asks their customers to share their opinions and their experiences on the products which they purchased. Unfortunately, this is a very cumbersome task to go through all review comments and to decide whether the product is up to the satisfaction level of customer or not. The main issue with these review comments is to manage all those comments and make a meaningful summarized form of review whether it represents a positive sense or feedback about the product or the negative or neutral. So, the main task is to build a dictionary of entities from these reviews. This book emphasize on making a model for Lexicon Matching using Hidden Markov Model (HMM) and Fuzzy K-Means Clustering. The outcomes of the results indicate that the trained HMM system is very promising in performing the desired tasks and achieved maximum possible precision and accuracy in case of Lexicon Matching.
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