Lexicons can be used when we don’t have sufficient training data to build a supervised sentiment analyzer; it can often be expensive to have a human assign sentiment to each document to train the supervised classifier. In such cases, lexicons can be used in a rule-based algorithm for classification.

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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

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

Speech and Language Processing (3rd ed. draft) 

Lexicons for Sentiment Recognition

The most common algorithms involve supervised classification: a training set is labeled for the affective meaning to be detected, and a classifier is built using features extracted from the training set. Many possible values can be used for lexicon features. The simplest is just an indicator function, in which the value of a feature $f_L$ takes the value 1 if a particular text has any word from the relevant lexicon $L$. 

Lexicons for Affect Recognition

A connotation frame, by contrast, is a lexicon that incorporates a richer kind of grammatical structure, by combining affective lexicons with frame semantic lexicons. The basic insight of connotation frame lexicons is that a predicate like a verb expresses connotations about the verb’s arguments.

Connotation Frames

This way of learning sentiment lexicons make use of two sets of seed words like good or bad which in turn will find out the similarity of a particular word to any of the two seed word sets.

There are two types of seed-based semi-supervised lexicon induction algorithms,

- Axis-based 

- Graph-based 

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