Jin et al, 2009

Citation

Jin, W., Ho, H.,Srihari, R., 2009, OpinionMiner: A Novel Machine Learning System for Web Opinion Mining and Extraction, KDD'09

Online version

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Summary

This paper introduces a system that mines customer reviews of a product and extract product features from the review. The system return opinion expression that are extracted from product review as well as opinion direction. Opinion mining have been studied widely in machine learning and information extraction community. Most of these approaches have used statistical or rule-based learning to extract opinion expression. Jin et al. in this work have introduced a new technique that uses lexicalized HMM for opinion mining.

System Architecture

The architecture of their system is as follow:

- Pre-processing: The system first crawls web pages from the Web, clean HTML files, and segments sentences. The technique that has been used to extract reviews of a product is not described in the paper and they have assumed that the reviews are given to the input of the learning system.

- Entity types and tag sets: They have defined four entity types for each product review: components (e.g. physical object of a camera), functions (e.g. zoom in a camera), features (e.g. color), and opinions (e.g. ideas and thoughts). For each if these types they have defined a set of tags that are used in annotation process.

- Lexicalized HMMs: Given a review of a product as an input of the system, the goal of lexicalized HMM is to assign appropriate tag type to each part of product review. For classification they maximize conditional probability $P(T|W,S)$ where T is the tags that we want to assign to different parts of product review, W is all the words in the review and S is the POS tag for each word. They have used MLE to learn parameters of the system.

- Information propagation: The goal of this part is to decrease the number of training data that this system requires. Suppose that we have sentence "Good picture quality" as part of a review in the training data. Word "good" is tagged as "<opinion_pos_exp>" in the training data. The system then adds more information by looking at a dictionary and substitute word "good" with it's synonyms. This idea is applied to all the words in the training data to extend the number of examples.

- Bootstrapping: The main contribution of this system is the bootstrapping part. The idea is to partition the training set to two different disjoint sets and train a HMM using each of these sets. Then for each instance of the test data (which is non annotated by the human), if two HMMs classify the input review to the same class and if the confidence value is above a threshold T then we add this new instance to the training example. This can significantly decrease the amount of time that human should spend to annotate training data.

Evaluation Results

They have tested their system