citation: @inproceedings{dataset_he_hebrew_sentiment,
      title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew",
      author = "Amram, Adam  and
          Ben David, Anat  and
          Tsarfaty, Reut",
      booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
      month = aug,
      year = "2018",
      address = "Santa Fe, New Mexico, USA",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/C18-1190",
      pages = "2242--2252",
      abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.",
  }
  