# Emotion Recognition on Call Center Voice Data
## 2 Related Work
### Deep Learning Approaches in Text-Based Emotion Recognition
This dataset was utilized to compute word statistics that are essential for text preprocessing, training word2 vec embeddings, and affective word embeddings. The empirical findings indicated that transfer learning did not surpass the performance of the model initialized randomly. Their model achieved the highest position among all the teams in the SemEval-2018 competition [28]. [PERSON] and [PERSON] introduced a deep learning approach that utilizes pretrained word embeddings to represent tweets. The embeddings were inputted into a gated recurrent unit (GRU), and the classification was achieved using a dense neural network. Their model obtained the 15 th position among the teams in the SemEval-2018 competition. [PERSON] and [PERSON] defined emotion recognition in tweets as a problem of classifying emotions into two categories.Tweet representation was achieved via word embeddings. The embeddings were inputted into four deep neural networks. The output of the fourth deep neural network was standardized to either one or zero using a threshold value of 0.5. Their model attained the 4 th position for Arabic and the 17 th position for English among the participating teams in the SemEval-2018 competition [36]. [PERSON] and [PERSON] proposed transforming a multi-label emotion recognition issue into a binary classification problem. This method facilitated the utilization of distinct model architectures and parameters for each individual emotion label. The authors employed three gated recurrent unit layers, with two of them being bidirectional. Given the large size of the training dataset, an autoencoder was constructed to utilize unlabeled tweets for the purpose of acquiring weights that could be employed in the classifiers. Pretrained embeddings were utilized to represent emojis, phrases, and hashtags. Their outcomes were above the baseline, although they fell short of the achievements of the other participants. Their model obtained the 24 th position among the teams in the SemEval-2018 competition [37]. [PERSON] et al. introduced a deep learning model designed to identify and classify emotions in textual conversations. Three pretrained embeddings, specifically word2 vec-twitter, GloVe, and ekphrasis, were utilized. The input to the model consists of an embedding layer, which is then sent via a bidirectional long short-term memory layer, followed by an attention layer and a convolutional neural network (CNN) layer. The results of the Bi-LSTM and the CNN are combined, and global max-pooling is performed. The pooling scores are inputted into a deep neural network that uses a softmax activation function to perform classification. The findings indicate that the utilization of pretrained embeddings resulted in enhanced performance. In addition, the model effectively acquired both local and long-term characteristics by merging the outputs of the Bi-LSTM and CNN layers [29]. [PERSON] et al. introduced a deep learning model that uses word embeddings to represent tweets. The embeddings were inputted into a long short-term memory model. The model assigned a score to each emotion label for classification and selected the labels with the highest three scores. Their model attained the 23 rd position in the SemEval-2018 competition [30]. [PERSON] et al. introduced a deep learning model designed to identify and classify emotions in written conversations. In order to address the issue of encountering words that are not included in the lexicon when utilizing pretrained word embeddings, the emojis are substituted with an appropriate term that conveys the intended emotion. The embeddings are inputted into a Bi-LSTM layer, while an attention mechanism enhances the importance of the emotion words. The inner product is derived from the output of the Bi-LSTM and the attention weights and then sent into another Bi-LSTM layer. Subsequently, the output of the Bi-LSTM layer undergoes global max-pooling and global average pooling and is processed by the final tensor.