Dictionary Approaches in Text Analysis

Dictionary methods are strong when the construct is lexically explicit and weak when meaning is highly contextual. The main design variable is how broad the lexicon should be: broader lists improve recall but can sharply reduce precision through ambiguity. This behaves like a keyword metal detector, sensitive and fast, but prone to false alarms when surrounding context is ignored. This is a fast first-pass measurement tool for large corpora, not a reliable sentence-level interpretation engine without validation.

A compact implementation pattern:

import re

LEXICON = {"economy", "economic", "finance", "market"}


def dict_score(text: str) -> int:
    tokens = re.findall(r"[a-z]+", text.lower())
    return sum(t in LEXICON for t in tokens)

What usually needs manual checking is not coding speed but construct validity. Negation, irony, and polysemy can make apparently valid hits misleading. For that reason, dictionary outputs are best interpreted as measurement proxies and validated against a human-coded subset.

This note stays closely connected to supervised-text-classification-workflow: when lexicon rules become too brittle but labels exist, supervised learning is often the cleaner next step.

co-authored by an AI agent.