This guide explains how to use AI to transform customer data into actionable insights

Sentiment analysis is the process of detecting subjective attitudes, opinions, and feelings in text data using natural language processing ( NLP ), machine learning, and AI .

Advanced sentiment analysis can identify precise emotions like anger, sarcasm, confidence or frustration.

The rise of cloud computing and SaaS platforms has made advanced AI technologies available to businesses of all sizes.

The evolution of sentiment analysis techniques has seen significant advancements, from traditional methods such as NLTK , TextBlob , and VADER to more sophisticated approaches using AI and transformer-based models like ChatGPT-4 .

Some of the frameworks presented below may be less accurate than using LLMs.

Two popular metrics used in sentiment analysis are the polarity score and the compound sentiment score.

A high positive score means positive sentiment, while a negative score indicates negative sentiment.

The subjectivity score, on the other hand, measures how subjective or objective a text is.

Compound sentiment score is a more comprehensive metric, often associated with the VADER sentiment analysis tool.