Обработка отрицаний и особых случаев

Counting cues breaks on not good, because good still scores positive. Negation can flip a sentence and fool a naive detector.

Keep a small list of negation words like not, never, and no. When one appears, the cue right after it usually means the opposite.

A simple fix: if a token is a negator, invert the sentiment of the word that follows it before adding it to your score.

Negation usually affects the next word or two, so apply a small window. Flipping the whole rest of the sentence overcorrects.

Words like very and really act as intensifiers. Very good feels stronger than good, so you can weight the next cue more heavily.

Some words soften feeling. Slightly and barely are dampeners that should shrink the weight of the cue they modify.

People pour emotion into emojis. A smiley adds positive signal and a frowning face adds negative, so map common ones into your lexicon. 🙂

Strong feeling shows up as repeated marks. Many bangs can raise the weight of nearby cues, since amazing screams louder than amazing.

Rules still miss sarcasm like just wonderful, another bug. Flag low-confidence cases for review; honest limits beat false certainty.

Long reviews praise and complain at once. A single label hides nuance, so consider reporting sentence-level sentiment for a clearer picture.

Each new rule patches one gap but adds complexity. When edge cases pile up, a trained model usually handles them more gracefully.

Apply the negation rule you just learned.

You hardened the detector against negation, intensifiers, emojis, and sarcasm with small rules and windows. When edge cases stack up, reach for a trained model. ✅