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Supervised and Unsupervised Aspect Category Detection for Sentiment Analysis with Co-occurrence Data.

Kim Schouten, Onne van der Weijde, Flavius Frasincar

    IEEE Transactions on Cybernetics
    |April 20, 2017
    PubMed
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    This study introduces two methods for summarizing online reviews by identifying aspect categories. An unsupervised approach achieved 67% accuracy, while a supervised variant reached 84%, improving product evaluation.

    Area of Science:

    • Natural Language Processing
    • Information Retrieval
    • Computational Linguistics

    Background:

    • Online consumer reviews are vital for purchase decisions, but their volume hinders comprehensive evaluation.
    • Automated summarization of reviews is needed to extract key product aspects efficiently.
    • Identifying general aspect categories within review sentences is a crucial subtask for review summarization.

    Purpose of the Study:

    • To present two novel methods for identifying general aspect categories in online consumer reviews.
    • To evaluate the performance of an unsupervised method using association rule mining.
    • To develop and assess a supervised variant for improved aspect category detection.

    Main Methods:

    • An unsupervised method employing association rule mining on co-occurrence frequency data from a review corpus.

    Related Experiment Videos

  • A supervised variant of the aspect category identification method, trained on labeled data.
  • Performance evaluation using standard metrics, comparing against baselines and existing approaches.

    • Main Results:

    • The unsupervised method achieved a 67% score, outperforming several simple and supervised baselines.
    • The supervised variant demonstrated superior performance, reaching an 84% score.
    • Both methods offer advancements in extracting aspect categories from unstructured review text.

    Conclusions:

    • The proposed methods effectively identify general aspect categories in online reviews, aiding summarization.
    • The supervised approach significantly outperforms unsupervised methods and existing techniques.
    • These findings contribute to the development of more effective review analysis and summarization frameworks.