Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN

• Edison Marrese-Taylor ^[1] ; Jorge A. Balazs ^[1] ; Yutaka Matsuo ^[1]
  1. [1] University of Tokyo

     University of Tokyo

     Japón

     
• Localización: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop / Alexandra Balahur Dobrescu (ed. lit.), Saif M. Mohammad (ed. lit.), Erik van der Goot (ed. lit.), 2017, ISBN 978-1-945626-95-1, págs. 102-111
• Idioma: inglés
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• Resumen

  • Video reviews are the natural evolution of written product reviews. In this paper we target this phenomenon and introduce the first dataset created from closed captions of YouTube product review videos as well as a new attention-RNN model for aspect extraction and joint aspect extraction and sentiment classification. Our model pro- vides state-of-the-art performance on as- pect extraction without requiring the usage of hand-crafted features on the SemEval ABSA corpus, while it outperforms the baseline on the joint task. In our dataset, the attention-RNN model outperforms the baseline for both tasks, but we observe im- portant performance drops for all models in comparison to SemEval. These results, as well as further experiments on domain adaptation for aspect extraction, suggest that differences between speech and writ- ten text, which have been discussed exten- sively in the literature, also extend to the domain of product reviews, where they are relevant for fine-grained opinion mining.