A novel lexicalized HMM-based learning framework for web opinion mining

ABSTRACT

Merchants selling products on the Web often ask their customers to share their opinions and hands-on experiences on products they have purchased. As e-commerce is becoming more and more popular, the number of customer reviews a product receives grows rapidly. This makes it difficult for a potential customer to read them to make an informed decision on whether to purchase the product. In this research, we aim to mine customer reviews of a product and extract highly specific product related entities on which reviewers express their opinions. Opinion expressions and sentences are also identified and opinion orientations for each recognized product entity are classified as positive or negative. Different from previous approaches that have mostly relied on natural language processing techniques or statistic information, we propose a novel machine learning framework using lexicalized HMMs. The approach naturally integrates linguistic features, such as part-of-speech and surrounding contextual clues of words into automatic learning. The experimental results demonstrate the effectiveness of the proposed approach in web opinion mining and extraction from product reviews.

REFERENCES

• 1: Xiaowen Ding , Bing Liu , Philip S. Yu, A holistic lexicon-based approach to opinion mining, Proceedings of the international conference on Web search and web data mining, February 11-12, 2008, Palo Alto, California, USA [doi>10.1145/1341531.1341561]
• 2: Guohong Fu , Kang-Kwong Luke, Chinese named entity recognition using lexicalized HMMs, ACM SIGKDD Explorations Newsletter, v.7 n.1, p.19-25, June 2005 [doi>10.1145/1089815.1089819]
• 3: Minqing Hu , Bing Liu, Mining and summarizing customer reviews, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014073]
• 4: Sang-Zoo Lee , Jun-ichi Tsujii , Hae-Chang Rim, Lexicalized hidden Markov models for part-of-speech tagging, Proceedings of the 18th conference on Computational linguistics, p.481-487, July 31-August 04, 2000, Saarbrücken, Germany [doi>10.3115/990820.990890]
• 5: Ana-Maria Popescu , Oren Etzioni, Extracting product features and opinions from reviews, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.339-346, October 06-08, 2005, Vancouver, British Columbia, Canada [doi>10.3115/1220575.1220618]
• 6: Peter D. Turney, Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews, Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, July 07-12, 2002, Philadelphia, Pennsylvania [doi>10.3115/1073083.1073153]
• 7: Peter D. Turney , Michael L. Littman, Measuring praise and criticism: Inference of semantic orientation from association, ACM Transactions on Information Systems (TOIS), v.21 n.4, p.315-346, October 2003 [doi>10.1145/944012.944013]
• 8: Li Zhuang , Feng Jing , Xiao-Yan Zhu, Movie review mining and summarization, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA [doi>10.1145/1183614.1183625]

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning

Additional Classification:
  G. Mathematics of Computing
  G.3 PROBABILITY AND STATISTICS
      Subjects: Markov processes

  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.7 Natural Language Processing
  I.5 PATTERN RECOGNITION
      I.5.2 Design Methodology
          Subjects: Classifier design and evaluation

General Terms:
Algorithms, Design, Performance, Theory