 - A weighted lexical graph is formed by connecting each word to its k nearest neighbors using cosine similarity.

 - A set of positive and negative seed words are chosen.

 - Propagate polarities from the seed set.

 - Create word scores with the help of negative and positive seed sets.

 - Use bootstrap sampling to assign confidence regions to the word score.


SentProp Algorithm

Defining lexicons by propagating sentiment labels on graphs. SentProp (Sentiment Propagation) algorithm is developed on this idea.

San Jose State University

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 

Semi-supervised Induction of Affect Lexicons

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

Speech and Language Processing (3rd ed. draft) 

Seed words like good or bad are given.

For a given word, ‘w’ to be labeled ,a measurement of how much each word 'w' is similar to the good word and different from the bad word is calculated.


Axis-based semi-supervised lexicon induction algorithms

Graph based Label Propagation

The core of semisupervised algorithms of induction of affect lexicons lies in the metric of measuring seed word similarities. Besides embedding cosine, there’re other methods.

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