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Text data is a favorable research object for emotion recognition when it is free and available everywhere in human life. Compare to other types of data, the storage of text data is lighter and easy to compress to the best performance due to the frequent repetition of words and characters in languages.
Sentiment analysis (also known as opinion mining or emotion AI) is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
The process of speech/text affect detection requires the creation of a reliable database, knowledge base, or vector space model, [21] broad enough to fit every need for its application, as well as the selection of a successful classifier which will allow for quick and accurate emotion identification.
Text Classification 1997 [18] [19] W. Loh et al. Vietnamese Students’ Feedback Corpus (UIT-VSFC) Students’ Feedback. Comments 16,000 Text Classification 1997 [20] Nguyen et al. Vietnamese Social Media Emotion Corpus (UIT-VSMEC) Users’ Facebook Comments. Comments 6,927 Text Classification 1997 [21] Nguyen et al.
In 2007, the Emotion Markup Language Incubator Group (EmotionML XG) was set up as a follow-up to the Emotion Incubator Group, "to propose a specification draft for an Emotion Markup Language, to document it in a way accessible to non-experts, and to illustrate its use in conjunction with a number of existing markups."
Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Emotion classification, the means by which one may distinguish or contrast one emotion from another, is a contested issue in emotion research and in affective science. Researchers have approached the classification of emotions from one of two fundamental viewpoints: [citation needed] that emotions are discrete and fundamentally different constructs
The emotion annotation can be done in discrete emotion labels or on a continuous scale. Most of the databases are usually based on the basic emotions theory (by Paul Ekman) which assumes the existence of six discrete basic emotions (anger, fear, disgust, surprise, joy, sadness). However, some databases include the emotion tagging in continuous ...