Sentiment Analysis

Sentiment analysis, also called opinion mining, assigns an attitude to a piece of text. The simplest version labels a message as positive, negative or neutral. More detailed versions score intensity, detect emotions such as anger or confusion, or measure sentiment per topic, for example positive about the product but negative about delivery. That last variant is known as aspect-based sentiment analysis.

Early tools counted positive and negative words from a dictionary. Machine learning models later learned from labelled examples. Today, language models judge tone in context, including negation such as “not bad at all”, although sarcasm, irony and mixed feelings remain hard.

The output is a probability, not a fact. It is most reliable when aggregated over many conversations rather than read message by message.

A customer writes: “Third time asking. Where is my refund?” A sentiment model flags this as strongly negative. In a support team, that flag could move the conversation up a queue, alert a colleague or change how a reply is worded. Across many chats, a high share of negative messages about one topic can point to a recurring problem.

Sentiment data helps teams prioritize unhappy customers and see which topics cause frustration. It is a signal, not a verdict: a short, neutral-sounding message can still come from an annoyed customer. Many teams combine it with direct feedback, such as CSAT surveys, and with reading real transcripts.

intoCHAT does not include sentiment analysis or sentiment scores. Its insights show conversations, messages and leads over time, and its chat logs offer search and full transcripts. Reading those transcripts shows how visitors phrase their questions and where answers fall short, so you can add or prioritize content to improve the agent.