Cavicchio F. Emotion Detection in Natural Language Processing 2025 torrent

Textbook in PDF format

This book provides a practical guide on annotating emotions in natural language data and showcases how these annotations can improve Natural Language Processing (NLP) and Natural Language Understanding (NLU) models and applications. The author presents an introduction to emotion as well as the ethical considerations on emotion annotation. State-of-the-art approaches to emotion annotation in NLP and NLU including rule-based, machine learning, and deep learning applications are addressed. Theoretical foundations of emotion and the implication on emotion annotation are discussed along with the current challenges and limitations in emotion annotation. This book is appropriate for researchers and practitioners in the field of NLP and NLU and anyone interested in the intersection of natural language and emotion.
In Deep Learning, we can leverage the neural network layers to discern relevant keywords from labelled and unlabelled emotion datasets. The process usually starts with tokenisation, removing stop words, and lemmatising the emotion dataset. After the dataset treatment, embeddings are created by assigning numerical values to the tokens. We then apply classification algorithms, inputting the numeric vectors into the deep neural network. Within the network, layers are aligned with corresponding emotion labels. Thus, the network learns to recognise patterns in the data, which it then applies to predict the associated emotion labels. For example, Shrivastava et al. presented a novel annotated dataset derived from transcripts of a TV show annotated using Ekman’s six basic emotions. The study employed a sequential CNN, which sequentially processes training data for emotion detection. This approach enables the network to leverage the characteristics of preceding sentences for emotion detection in subsequent ones. The initial layer of the network uses pre-trained word embeddings to transform words into vectors, ensuring that words with similar meanings have similar vector representations. The network’s ability to grasp semantic and contextual nuances is further enhanced by feeding context features into an attention model. The attention model assesses the relevance of the context in the analysis. Integrating the attention mechanism with CNN allows the model to selectively focus on the most informative features within the input sequences. The attention mechanism in the proposed model mainly aids the CNN in concentrating on words or features that significantly impact the classification