# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Text-Based Emotion Recognition  
The field of machine learning, known as deep learning, is a subfield of machine learning in which computer programs learn from their experiences and comprehend the world through the lens of a hierarchy of concepts. Each concept is defined in terms of its relationship to concepts that are more straightforward. Through the use of this method, a computer program is able to acquire more complex ideas by constructing them on the basis of more basic ones. Long short-term memory (LSTM) is the deep learning model that is utilized the most in this context. LSTM is a specialized type of recurrent neural network (RNN) that has the ability to efficiently manage long-term dependencies. It is possible for LSTM to solve the problem of vanishing or exploding gradients that are typical of RNNs. In [35], the primary steps of the LSTM algorithm for emotion recognition in text are presented. The first step is to preprocess text on the dataset containing the emotions. Lemmatization, tokenization, and the removal of stop words are all possible procedures that are included in the preprocessing steps. Following this step, the embedding layer is constructed and then fed into one or more layers of the LSTM classifier. After that, the output is fed into a dense neural network (DNN) that has units equal to the number of emotion labels and a sigmoid activation function in order to carry out the classification. [PERSON] et al. introduced a deep learning model designed to identify and classify emotions in written conversations. The four submodels that make up the model are as follows: the sentence-encoder submodel, the three-input submodel (INP3), the two-output submodel (OUT2), and the bidirectional encoder representations from transformers (BERT) submodel. The INP3 submodel processes each segment of the dialog by inputting it into two Bi-LSTM layers, and then applying an attention layer. The results are combined and inputted into three DNNs. The OUT2 submodel shares the identical architecture with the INP3 submodel. However, the three sections of the conversation are combined and treated as a single input. Furthermore, an extra DNN is incorporated subsequent to the attention layer to generate an additional output, specifically, a classification of the dialog as either emotional or non-emotional. The objective of this submodel is to mitigate the impact of an imbalanced dataset. The sentence-encoder submodel utilizes a feed-forward network that incorporates a fine-tuned universal sentence encoder (USE). It selectively employs only the first and third segments of the dialog. The BERT submodel approaches the problem by representing it as a sentence-pair classification task, utilizing only the first and third utterances of the dialog. This submodel is integrated with a lexical normalization mechanism. Various classification techniques were evaluated, including support vector machines (SVM), SVM with normalization (SVM-n), logistic regression, naive Bayes, JRip rule learner, random forest, and J48. The results indicate that the features acquired by the INP3 and OUT2 submodels yield superior performance compared to the characteristics acquired by the USE and BERT submodels. Nevertheless, the combination of the four submodels yields the most optimal performance outcome using SVM-n [27]. [PERSON] et al. introduced a deep learning model designed for the purpose of recognizing many emotions in English tweets. Their model comprised a Bi-LSTM with two layers, which was enhanced with a multilayer self-attention mechanism. They employed the ekphrasis tool to analyze the text. The preprocessing procedures encompassed Twitter-specific tokenization, spell correction, word normalization, word segmentation, and word annotation. Given the scarcity of training data, they employed a transfer learning method by initially training the Bi-LSTMs on the SemEval-2017, Task 4A dataset. In addition, they gathered a dataset consisting of 550 million English tweets.