Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets
This research addresses the problem of accurately classifying and predicting emotion intensity in tweets for social media analysis, offering an incremental improvement over existing methods.
This paper explores deep learning and transfer learning for emotion analysis in tweets, combining LSTM and CNN for classification and extending it for intensity prediction. The proposed models outperform state-of-the-art in emotion classification and achieve competitive results in emotion intensity prediction.
In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models. The proposed approach for emotion analysis combines a Long Short Term Memory (LSTM) network with a Convolutional Neural Network (CNN). Then we extend this approach for emotion intensity prediction using transfer learning technique. Furthermore, we propose a technique to visualize the importance of each word in a tweet to get a better understanding of the model. Experimentally, we show in our analysis that the proposed models outperform the state-of-the-art in emotion classification while maintaining competitive results in predicting emotion intensity.