OpinionMiner: a novel machine learning system for web opinion mining and extraction

ABSTRACT

Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. Unfortunately, reading through all customer reviews is difficult, especially for popular items, the number of reviews can be up to hundreds or even thousands. This makes it difficult for a potential customer to read them to make an informed decision. The OpinionMiner system designed in this work aims to mine customer reviews of a product and extract high detailed product entities on which reviewers express their opinions. Opinion expressions are identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that employed rule-based or statistical techniques, we propose a novel machine learning approach built under the framework of lexicalized HMMs. The approach naturally integrates multiple important linguistic features into automatic learning. In this paper, we describe the architecture and main components of the system. The evaluation of the proposed method is presented based on processing the online product reviews from Amazon and other publicly available datasets.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

Additional Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
          Subjects: Text analysis

General Terms:
Algorithms, Design, Experimentation, Human Factors

Keywords:
lexicalized HMMs, opinion mining, sentiment analysis