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I and a group of people are developing a Sentiment Analysis Algorithm. I would like to know what are the existent ones, because I want to compare them. Is there any article that have the main algorithms in this area?
Thanks in advance
Thiago
612
There are multiple machine learning algorithms used for sentiment analysis like Support Vector Machine (SVM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Random Forest, Naïve Bayes, and Long Short-Term Memory (LSTM), Kuko and Pourhomayoun (2020).
Basically, there are three types of sentiments — “positive”, “negative” and “neutral” along with more intense emotions like angry, happy and sad or interest or not interested etc. Further you can find here more refined sentiments used to analyze the sentiments of the people in different scenarios.
Some of the papers on sentiment analysis may help you -
For quick implementation naive bayes is recommended. You can find an example here http://nlp.stanford.edu/IR-book/
We did a statistical comparision of various classifiers and found SVM to be most accurate, though for a dataset consisting of large contents ( http://ai.stanford.edu/~amaas/data/sentiment/ ) none of the methods worked well.Our study may not be accurate though. Also instead of treating sentiment analysis as a text classification problem, you can look at extraction of meaning from text, though I do not know how successful it might be.
102
apparently the NLTK, a python natural language processing library, has one:
http://text-processing.com/demo/sentiment/
Probably worth having a look at it.
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