How GPT Models Can Improve Sentiment Analysis & Algorithm Performance

What Is Sentiment Analysis

In recent years, the applications of sentiment analysis have grown exponentially as more and more businesses seek to understand the emotions of their customers.

At its core, sentiment analysis is the process of extracting and analysing emotions from text data published on several sources such as social media, forums, comments, and customer reviews.

Sentiment analysis has become a must-have tool for understanding and managing customer emotions for brand reputation, customer service or political campaigns. In this blog post, we’ll take a look at how GPT models improve algorithm performance for sentiment analysis.

Sentiment Analysis Benefits

Sentiment analysis consists of extracting text from the web (or other sources) and analysing it to understand the feelings and thoughts of customers, employees, community, voters or any other group of people.

The ability to understand emotion is powerful because businesses, politicians, and financial operators can use that to improve customer satisfaction, employee engagement, and even political campaigns.

How GPT Models Improve Algorithm Performance

GPT models are machine learning models that are designed to generate text. They are trained on large amounts of data (big data) and can generate new text similar to the training data. You can use GPT models for sentiment analysis because they can create text that contains emotional information.

For example, you can train a GPT model on a data set of customer reviews collected for you through web scraping, where an AI agent builds the extraction pipeline from a plain-language description and validates it on real rows, integrated with a model training tool.

The model would learn the sentimental language used in the reviews and could then generate new reviews with similar sentiments. This allows businesses to automatically generate large quantities of sentiment-rich text data for analysis.

Today the same family of models goes further: instead of only generating text, they can read and score each review, post or comment directly, explaining why a piece of text is positive or negative. If the data must stay in-house, open models can be served and fine-tuned in your own environment with Private AI.

How are you using the millions of data available nowadays on the market and Internet? If you’re looking for a way to better understand the emotions of your clients, prospects, staff members and collaborators, or the citizens of a State, sentiment analysis is a valuable tool you should start using to gain competitive advantage and avoid mistakes.

On WebRobot, sentiment analysis is available as a ready-made vertical: AI agents pick the sources from a curated acquisition catalogue, collect the texts on a schedule within each source's terms of use, and score them, with human approval on the critical steps.

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