BERT based Text Classification

BERT-based text classification fine-tunes a pretrained bidirectional transformer language model on labelled examples so that it assigns documents, sentences, or reviews to predefined categories such as sentiment or topic.

BERT 기반 텍스트 분류는 사전학습된 양방향 트랜스포머 언어모형을 라벨이 있는 예시로 미세조정하여 문서, 문장, 리뷰를 감성이나 주제 같은 미리 정한 범주에 배정하는 방법입니다.

How do I automatically classify texts such as reviews or open-ended responses into categories?

리뷰나 개방형 응답 같은 텍스트를 범주별로 자동 분류하려면 어떻게 하나요?

BERT-based text classification adapts a pretrained transformer language model to a specific labelling task, using context from the whole sentence to assign each text to a category. It fits when a set of labelled texts is available and wording or context matters, and it usually outperforms bag-of-words models when enough labelled examples per class exist.

When to use BERT based Text Classification

• Sentiment or stance classification of reviews, posts, or comments
• Coding open-ended survey responses into predefined categories
• Texts where context and word order affect meaning

When to use something else

• Very few labelled texts or limited computing resources: use TF-IDF with logistic regression
• No predefined categories: use topic modeling such as LDA or BERTopic

Assumptions and requirements

• Labelled training texts for every class, with reasonable balance
• Pretrained model that matches the language of the texts
• Texts truncated or split to the model's maximum input length
• Held-out test set for evaluation

What the analysis reports

• Predicted labels and class probabilities
• Accuracy, precision, recall, and F1 for each class
• Macro-averaged F1
• Confusion matrix

How to report it

The fine-tuned BERT classifier achieved a macro-averaged F1 score of [value] and an accuracy of [value] on the held-out test set.

Related methods

• TF-IDF Analysis
• Naive Bayes Classification
• Topic Modeling (LDA)

Run BERT based Text Classification on Quorant