Text Classification

How text classification works

A classifier learns a mapping from raw text to a fixed set of target labels. In the supervised setup, you assemble examples that have already been labeled, say a few hundred snippets tagged pricing change, new feature, or executive hire, and the model learns the patterns of wording that separate one category from another. At inference time it reads a new document and returns the most probable label, or a probability across all labels.

Classical approaches represented text as bag-of-words or TF-IDF vectors and fed them to Naive Bayes, logistic regression, or a support vector machine. Modern approaches encode the text with a transformer such as BERT, which captures word order and context, and attach a classification head. Large language models add a third mode: given the label definitions in a prompt, they can classify with no task-specific training data at all, which is why zero-shot and few-shot classification have become common in change-monitoring tools that need to add new categories quickly.

Classification also splits by output shape. Single-label classification assigns exactly one category per document; multi-label classification allows several at once, so a competitor's announcement can be both a pricing change and a new feature.

Text classification vs. topic modeling

The two are easy to conflate because both organize text into groups, but they sit on opposite sides of the supervised-unsupervised divide. Text classification is supervised: you define the label set in advance, meaning the exact categories your alerts need, and the model learns to sort documents into those known buckets. Topic modeling, such as Latent Dirichlet Allocation, is unsupervised: it discovers latent themes across a corpus without any predefined labels, and you interpret the clusters after the fact.

The practical consequence is control. Classification gives you named, stable categories you can build routing rules and dashboards on, at the cost of needing labeled examples or clear label definitions up front. Topic modeling needs no labels and is good for exploring an unfamiliar corpus, but its clusters shift as the data changes and rarely map cleanly onto the operational categories a CI team actually alerts on. Sentiment analysis is a third relative, simply text classification whose label set is opinion polarity rather than a general topic.

How competitive-intelligence teams use it

In a competitor-tracking workflow, classification is one stage of a pipeline, not the whole thing. Change detection flags that a competitor's page, post, or job listing differs from its last snapshot. Text classification then labels the nature of that change: was it a pricing-page edit, a new product feature, a leadership hire, or a shift in positioning language. Importance or significance scoring decides whether the labeled change is worth surfacing, and alerting delivers it.

The label is what makes the rest of the system usable. It lets a user subscribe to only pricing changes across their tracked set, route executive-hire signals to the sales team and messaging shifts to product marketing, and build a digest grouped by change type instead of by URL. Without a reliable category, every diff looks the same and the feed collapses into noise. This is why classification quality is closely tied to signal-to-noise: a misfiled pricing change either buries an important signal or clutters a channel meant for something else. meertrack's alert filtering depends on this labeling step to turn continuous website, pricing, job-posting, and news monitoring into categories a team can actually triage.

Common mistakes and limitations

The most common failure is a label set that does not match reality. Categories that overlap (is a new add-on tier a pricing change or a new feature?) or that leave no home for edge cases force the model into arbitrary calls and erode trust in the feed. Defining mutually clear categories, and allowing multi-label output where a change genuinely spans two, prevents most of this.

Classifiers also degrade when the incoming text drifts away from what they were trained on. A model tuned on one industry's pricing language may misread another's; a competitor that rewrites its site in a new style can quietly tank accuracy until the model is re-evaluated. Class imbalance compounds this, because rare but high-value categories like executive hires have few training examples, so they are exactly the ones a classifier is most likely to miss. Finally, classification labels the document as a whole; it does not extract the specific entities or numbers inside it. Pulling out which price changed or who was hired is the separate job of named entity recognition and information extraction downstream.

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