@inproceedings{huang-etal-2020-weakly,
    title = "Weakly-Supervised Aspect-Based Sentiment Analysis via Joint Aspect-Sentiment Topic Embedding",
    author = "Huang, Jiaxin  and
      Meng, Yu  and
      Guo, Fang  and
      Ji, Heng  and
      Han, Jiawei",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2020.emnlp-main.568/",
    doi = "10.18653/v1/2020.emnlp-main.568",
    pages = "6989--6999",
    abstract = "Aspect-based sentiment analysis of review texts is of great value for understanding user feedback in a fine-grained manner. It has in general two sub-tasks: (i) extracting aspects from each review, and (ii) classifying aspect-based reviews by sentiment polarity. In this paper, we propose a weakly-supervised approach for aspect-based sentiment analysis, which uses only a few keywords describing each aspect/sentiment without using any labeled examples. Existing methods are either designed only for one of the sub-tasks, or are based on topic models that may contain overlapping concepts. We propose to first learn {\ensuremath{<}}sentiment, aspect{\ensuremath{>}} joint topic embeddings in the word embedding space by imposing regularizations to encourage topic distinctiveness, and then use neural models to generalize the word-level discriminative information by pre-training the classifiers with embedding-based predictions and self-training them on unlabeled data. Our comprehensive performance analysis shows that our method generates quality joint topics and outperforms the baselines significantly (7.4{\%} and 5.1{\%} F1-score gain on average for aspect and sentiment classification respectively) on benchmark datasets."
}