YNU-HPCC at EmoInt-2017: Using a CNN-LSTM Model for Sentiment Intensity Prediction

• You Zhang ^[1] ; Hang Yuan ^[1] ; Jin Wang ^[1] ; Xuejie Zhang ^[1]
  1. [1] Yunnan University

     Yunnan University

     China

     
• 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. 200-204
• Idioma: inglés
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• Resumen

  • The sentiment analysis in this task aim- s to indicate the sentiment intensity of the four emotions (e.g. anger, fear, joy, and sadness) expressed in tweets. Com- pared to the polarity classification, such intensity prediction can provide more fine- grained sentiment analysis. In this paper, we present a system that uses a convolu- tional neural network with long short-term memory (CNN-LSTM) model to complete the task. The CNN-LSTM model has t- wo combined parts: CNN extracts local n-gram features within tweets and LST- M composes the features to capture long- distance dependency across tweets. Our submission ranked tenth among twenty t- wo teams by average correlation scores on prediction intensity for all four types of e- motions.