Abstract
The field of natural language processing (NLP) is one of the first to be addressed since artificial intelligence emerged. NLP has made remarkable advances in recent years thanks to the development of new machine learning techniques, particularly novel deep learning methods such as LSTM networks and transformers. This chapter presents an overview of how deep learning techniques have been applied to NLP in the area of affective computing. The chapter examines traditional and novel deep learning architectures developed for natural language processing (NLP) tasks. These architectures comprise recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and the cutting-edge transformers. Moreover, a methodology for NLP method training and fine-tuning is presented. The chapter also integrates Python code that demonstrates two NLP case studies specializing in the educational domain for text classification and sentiment analysis. In both cases, the transformer-based machine learning model (BERT) produced the best results.
TopAffective Computing
Since the invention of the computer, researchers, and scientists in the field of computer science have been searching for ways to make computers interact with people in the same way that humans do, and as a result, many branches of study have been created in this field, one of which is Affective Computing.
Affective computing is a multidisciplinary field that includes psychology, cognitive psychology, and computer science within its areas of study. According to Picard (1997), affective computing is the field of study that focuses on making computers capable of recognizing and expressing emotions like a human being, as well as developing the ability to send intelligent responses to the emotions expressed by humans.
Key Terms in this Chapter
Sentiment Analysis: This task involves identifying and categorizing the emotions conveyed in a given text.
Dataset: A dataset is a collection of logically related data, which may include various types of information such as numerical values, textual details, images or videos.
Machine Learning (ML): Is a computer science field that enables computers to learn without explicit programming.
Deep Learning (DL): It is a subfield of machine learning that uses artificial neural networks to learn from analyzed data.
Affective Computing: It is a field of computer science that focuses on the development of systems and devices that enable computers to recognize, interpret and respond to human emotions as if another human did.
Natural Language Processing (NLP): A subfield of artificial intelligence that studies how computers perceive natural languages. It involves the development of techniques that allow computers to understand human language and even generate it.
Artificial Intelligence (AI): Is a branch of computer science that aims to create systems capable of carrying out tasks that typically demand human intelligence, including perception, reasoning, and learning.