But what if you could predict customer behavior, reduce churn, and even enhance the lifetime value of your clients just by interpreting feedback effectively?

Sentiment analysis is not just about understanding whether a response is positive or negative; it’s a sophisticated AI way to predict future customer actions and tailor your business strategies accordingly.

In this article, we delve into the realm of Natural Language Processing models that are not just capable but specifically engineered for feeling. Join us as we unpack their potential, compare their capabilities, and reveal how they can be the key to transforming customer testimonials into actionable business insights — all within a predictable 30-day timeframe.

But, before we get started, if you’re looking for a deeper dive into rapid AI software launch strategies that will accelerate your business model on my LinkedIn.

Rule-Based Models

The field of artificial intelligence has been around for nearly 100 years with sentiment analysis being one of the more recent advancements.

Rule based models are where it all started. These models operate on a set of predefined rules and lists of positive and negative words. Commonly they are very transparent and easy to interpret.

Though they often lack nuance and can miss the complex context or sarcasm that comes with natural language. This leads them to being unable to adapt to the constant changes associated with language. We call these kinds of models “brittle”.

Problems like these led to the development of more generalized models that can change with the evolving slang and sentiment.

Machine Learning Models

Am I the only one who didn’t know that machine learning models were used for sentiment analysis before neural nets were used?

These include models like Naive Bayes, SVM, and random forests. Learning from labeled data to capture more nuanced patterns than rule-based systems. This comes at the cost of requiring a substantial amount of labeled data to perform well and can sometimes be a “black box,” making interpretability harder.

The way these models work compared to rule-based models is through learning algorithms. Instead of hard coding values to specific words the ML model is given a set of initial criteria to evaluate the sentiment. From there it assigns it’s own meaning and sentiment for specific words and phrases.

Transformer Models

It really doesn’t have to be complicated. Attention is all you need.

This is the case made with the introduction of transformer models. These models can process large amounts of text in parallel using an “attention mechanism” to spend more time recognizing the relationships and dependencies among words and sentences.

State-of-the-art transformer models like BERT, GPT, and RoBERTa have set new benchmarks in sentiment analysis. They are very powerful at capturing context and can work with smaller amounts of labeled data when fine-tuned.

Requiring significant computational resources they can still struggle with very subtle sentiment or when the sentiment is heavily dependent on broader context or external knowledge.

Want a free guide to nail product-market fit so that you can launch your own AI software in just 8 weeks? Head over to my LinkedIn and grab a free copy

All the best,

-Adrian