Question: To prove that the model proposed by the author can improve the performance of emotion detection, what assumptions does the author make?
Evidence: A lot of work has been done on detecting emotion in speech or visual data BIBREF10 , BIBREF11 , BIBREF12 , BIBREF13 . But detecting emotions in textual data is a relatively new area that demands more research. There have been many attempts to detect emotions in text using conventional machine learning techniques and handcrafted features in which given the dataset, the authors try to find the best feature set that represents the most and the best information about the text, then passing the converted text as feature vectors to the classifier for training BIBREF14 , BIBREF15 , BIBREF16 , BIBREF17 , BIBREF18 , BIBREF19 , BIBREF20 , BIBREF21 , BIBREF22 , BIBREF23 , BIBREF24 , BIBREF25 , BIBREF26 . During the process of creating the feature set, in these methods, some of the most important information in the text such as the sequential nature of the data, and the context will be lost.
Considering the complexity of the task, and the fact that these models lose a lot of information by using simpler models such as the bag of words model (BOW) or lexicon features, these attempts lead to methods which are not reusable and generalizable. Further improvement in classification algorithms, and trying out new paths is necessary in order to improve the performance of emotion detection methods. Some suggestions that were less present in the literature, are to develop methods that go above lexical representations and consider the flow of the language.
Due to this sequential nature, recurrent and convolutional neural networks have been used in many NLP tasks and were able to improve the performance in a variety of classification tasks BIBREF27 , BIBREF28 , BIBREF29 , BIBREF30 . There have been very few works in using deep neural network for emotion detection in text BIBREF31 , BIBREF32 . These models can capture the complexity an context of the language better not only by keeping the sequential information but also by creating hidden representation for the text as a whole and by learning the important features without any additional (and often incomplete) human-designed features.
In this work, we argue that creating a model that can better capture the context and sequential nature of text , can significantly improve the performance in the hard task of emotion detection. We show this by using a recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole, and we show that this can improve the final performance significantly. Based on that, we suggest focusing on methodologies that increase the quality of these latent representations both contextually and emotionally, can improve the performance of these models. Based on this assumption we propose a deep recurrent neural network architecture to detect discrete emotions in a tweet dataset. The code can be accessed at GitHub [https://github.com/armintabari/Emotion-Detection-RNN].
In this work, we argue that creating a model that can better capture the context and sequential nature of text , can significantly improve the performance in the hard task of emotion detection. We show this by using a recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole, and we show that this can improve the final performance significantly.
Answer: They show this by using a recurrent neural network-based classifier that can learn to create a more informative latent representation of the target text as a whole, and they show that this can improve the final performance significantly.