Computational emotion models in NLP are mostly based on two families of emotion theories:

1)	Basic emotions theories

2)	Multi-dimension space theories


Main Emotion Theories used by Computational Emotion Models

Emotion can be defined as a relatively brief episode of response to the evaluation of an external or internal event. In machine learning, detecting emotions can improve many language processing tasks. For example, an online tutoring system can use emotion detection to see if a student is happy, sad, or bored. And detecting emotions in customer reviews can help businesses improve based on customer feedback.

University of Notre Dame

Sentiment analysis (also known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information. 


Sentiment analysis is widely applied to voice of the customer materials such as reviews and survey responses, online and social media, and healthcare materials for applications that range from marketing to customer service to clinical medicine.

Sentiment Analysis

An on-going but a helpful book resource about NLP
https://web.stanford.edu/~jurafsky/slp3/

Speech and Language Processing (3rd ed. draft) 

Research papers, articles and examples about sentiment analysis in NLP

Sentiment Analysis References

In cases that we do not have sufficient labeled data using all words in training set for sentiment classification, we can derive the word features from sentiment lexicons, which are external sources of words with sentiment labels. A common way is to add the features to indicate whether the word instance has occurred in the positive/ negative lexicon N times.


Sentiment Lexicons

Clip the frequency of words in each document at 1, as binary occurrence of a word seems to matter more than its frequency in some text classification tasks, such as sentiment analysis. Duplicate words are then removed before concatenating the words into a document.


Binary Multinomial Naive Bayes (Binary NB)

Sentiment Analysis can be viewed as a classification task with two classes, that is, positive and negative. Naive Bayes Classifier can be modified as Binary Multinomial Naive Bayes Classifier to improve its performance on sentiment analysis.

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