What is sentiment analysis?
Sentiment analysis is a machine learning tool that analyzes texts for polarity, from positive to negative. By training machine learning tools with examples of emotions in text, machines automatically detect sentiment without human input. In other words, machine learning allows computers to learn new tasks without being expressly programmed to perform them. Sentiment analysis models can be trained to read beyond definitions, to understand things like context, sarcasm, and misapplied words.
What are the algorithms used to perform sentiment analysis?
- Naïve Bayes: It is a simple group of probabilistic algorithms that, for sentiment analysis classification, assign a probability that a given the word or phrase should be considered positive or negative.
- Linear Regression: It calculates how the X input, i.e., words and phrases, relates to the Y output, i.e., polarity. It will determine where words and phrases fall on a polarity scale from ‘positive’ to ‘negative’ and everywhere in between.
- Support Vector Machines (SVM): It is another supervised machine learning model, like linear regression but more advanced. SVM uses algorithms to train and classify text within the sentiment polarity model, taking it beyond X/Y prediction.