Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks (2020)

Abstract: In sentiment analysis, several researchers have used emoji and hashtags as specific forms of training and supervision. Some emotions, such as fear and disgust, are underrepresented in the text of social media. Others, such as anticipation, are absent. This research paper proposes a new dataset for complex emotion detection using a combination of several existing corpora in order to represent and interpret complex emotions based on the Plutchik’s theory. Our experiments and evaluations confirm that using Transfer Learning (TL) with a rich emotional corpus, facilitates the detection of complex emotions in a four-dimensional space. In addition, the incorporation of the rule on the reverse emotions in the model’s architecture brings a significant improvement in terms of precision, recall, and F-score.

## (international workshop on semantic evaluation) the authors @xcite studied the relationship between words and emoticons. they also proposed an approach to predict the most likely emoji associated with a tweet. this proposed approach was based on a bidirectional long short-term memory (bilstm) architecture (bilstm). @xcite used a model that extends the recurrent convolutional neural network (rcnn) using finely-tuned external word representations and deepmoji phrase representations on the emotion detection task in semeval-2019. other work @xcite proposed a method to learn to incorporate specific words in word embeddings and showed an improvement in the performance especially when combining other sets of existing features. in our knowledge, none of the previous works considered the case of texts with conflicting emotions, hence the need for such a model.