NLP

Transformer sentiment classification algorithm

This NLP algorithm flowchart describes a transformer-based sentiment classifier for product reviews. Labeled reviews are normalized and split into training, validation, and test sets before the model sees them. The tokenizer creates token IDs and attention masks for the pretrained transformer encoder, and a classification head is fine-tuned on the training set.

UPDATED 2026-09-23

TYPEFlowchart

USE-CASENlp Algorithms

EXAMPLETransformer sentiment classification algorithm

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CASE ANALYSIS

Scenario

A product team trains a transformer classifier to label incoming review sentiment. The flow keeps the held-out test set separate until a candidate passes the validation threshold.

Key decisions

Dataset split: Training, validation, and test records are separated before modeling.
Attention masks: The tokenizer passes both token IDs and padding masks to the encoder.
Release gate: Validation F1 determines whether a candidate may proceed.
Held-out test: The test set is evaluated once after approval to avoid selection bias.

When to reuse this

Use this for supervised sentiment classification using a pretrained transformer. It is suitable for product reviews, survey comments, or other short labeled text.

FAQ

Frequently asked questions

Why use a separate test set?01

It gives an unbiased final estimate after model choices have been made using validation data.

What are attention masks?02

They tell the transformer which token positions are real text and which are padding.