Free lead sentiment analysis

Sentiment analysis is reading text automatically to work out whether the feeling behind it is positive, negative or neutral. It is applied to anything written in free text — support tickets, reviews, survey answers, sales notes — and turns that text into a score you can sort, count and track. The output is usually a number between -1 and +1 plus a label.

There are two main approaches. Lexicon methods score text against a dictionary of words with known positive or negative weights, adjusting for negations like “not good” and intensifiers like “very”. Machine learning methods learn the mapping from examples that people have already labelled. Lexicons are fast, need no training data and let you see exactly why a score came out as it did; learned models handle nuance better but need labelled examples and are harder to explain.

Lead scoring ranks prospects by how likely they are to buy, so a sales team works the best ones first. Scores usually combine fit — company size, industry, job title — with behaviour, such as pages viewed, emails opened and meetings booked. Basic setups assign points by hand; predictive scoring learns the weights from which leads actually closed. The point is to prioritise, not to eliminate.

Grading measures how well a lead matches your ideal customer profile; scoring measures how interested they are. A director at a 500-person target account grades highly even if they have done nothing yet. Someone downloading every guide from a company you cannot sell to scores highly but grades badly. Most teams use both, often as a grid, because acting on either one alone wastes time.

On clear, opinionated text like product reviews, good systems agree with human raters roughly 80-90% of the time. On short, terse business writing it is worse, and it struggles most with sarcasm, negation over long sentences and domain terms that carry a meaning the general dictionary does not know. It is reliable for spotting trends across thousands of records and unreliable for judging any single one, so use it to sort, then read.

It helps, but only as one signal among several. Negative language in recent notes correlates with deals that stall, and a sudden drop in tone across a pipeline is worth investigating. On its own it is weak, because sales notes record actions more often than feelings, and a rep's writing style affects the score as much as the customer's mood does. Read it alongside recency, stage movement and engagement.

Mostly for triage. Teams use it to surface the accounts whose notes have turned negative, to spot deals that have gone quiet, to check the tone of a rep's pipeline before a forecast call, and to find recurring complaints across hundreds of records that nobody would read in full. The value is in ranking a large pile of text so a person knows where to start.

Sentiment measures how someone feels; intent measures what they are trying to do. “This is frustrating” is negative sentiment. “How do I cancel?” is neutral in tone and a much stronger buying — or leaving — signal. Intent is generally the better predictor of what happens next, which is why the two are usually read together rather than one instead of the other.