Sentiment Analysis

What is sentiment analysis?

Sentiment analysis is the automated process of reading a piece of text and working out the emotional tone behind it, most often classifying it as positive, negative, or neutral. It is a branch of natural language processing, the field concerned with getting software to understand human language, applied specifically to the question of how the writer feels.

The appeal is scale. A person can read a handful of reviews or support messages and sense the mood, but they cannot read tens of thousands. Sentiment analysis reads all of them, consistently, and turns a wall of free text into something you can count and track. That makes it a practical way to hear what customers are feeling across every channel at once.

How sentiment analysis works

Under the bonnet, sentiment analysis has evolved through a few approaches, and most modern tools blend them.

• Lexicon-based methods score text against a dictionary of words tagged as positive or negative. They are fast and transparent but struggle with context, negation, and slang.
• Machine-learning models are trained on large sets of example text that people have already labelled, learning the patterns that separate a happy message from an unhappy one.
• Large language models now handle much of this work, reading a message in context and picking up nuance that simpler methods miss.

Analysis can also work at different levels of detail: a whole document, a single sentence, or a specific aspect within a sentence. Aspect-based analysis is the most granular, letting a single review register as positive about delivery but negative about price. Even the best models find sarcasm, mixed emotions, and heavy context genuinely hard, so scores are best read as a strong signal rather than a verdict.

How to apply sentiment analysis in support

In customer support, sentiment analysis earns its place by turning tone into action.

Prioritise the customers who need you most. Scoring incoming messages by sentiment lets a frustrated customer jump the queue ahead of a routine query, so the people closest to churning get attention first.

Feed your voice-of-the-customer programme. Aggregated sentiment across channels is a core input to voice of the customer, showing which topics drive dissatisfaction and where to focus improvements.

Strengthen quality and coaching. Tracking sentiment through a conversation, from a negative opening to a positive close, is a useful signal alongside Auto QA for spotting which interactions recovered well and which did not.

Close the loop. Sentiment trends make an excellent trigger for a customer feedback loop: a spike in negativity around one topic points straight to the fix, and tracking the same measure afterwards shows whether the fix worked.

The most reliable way to use sentiment analysis is to pair it with human judgement. Let the model surface where feeling runs strongest, then have a person read those conversations and decide what to do.