YNU-HPCC at EmoInt-2017: Using a CNN-LSTM model for sentiment intensity prediction

Abstract

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