Tokenization’dan TF-IDF’e: Metin Verisini Makine Öğrenmesine Hazırlamak

2026-03-18 · Source: NLP on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, short

Summary

This content outlines the fundamental steps for preparing raw text data for machine learning models within an NLP pipeline. It details text cleaning, tokenization, and stopword removal as initial processing stages to transform messy text into processable data. The article then explains three numerical representation methods: One-Hot Encoding, TF-IDF for weighting term importance, and Word2Vec for capturing semantic relationships in a multi-dimensional space. A practical mini-project demonstrates building a sentiment analysis model from scratch, utilizing TF-IDF features with Logistic Regression for training, evaluation, and prediction. This comprehensive guide provides the "skeleton" of a real NLP project, setting the stage for future exploration of advanced architectures like Transformers, BERT, and GPT.

Key takeaway

This guide outlines a complete NLP pipeline, from text cleaning and tokenization to stopword removal and numerical representation methods. It compares One-Hot, TF-IDF (for term importance), and Word2Vec (for semantic context) for vectorization. This enables building a sentiment analysis model from scratch using TF-IDF with Logistic Regression, providing a practical skeleton for real-world NLP projects.

Topics

• Natural Language Processing
• Text Preprocessing
• Tokenization
• TF-IDF Vectorization
• Word Embeddings

Best for: AI Student, Machine Learning Engineer, Data Scientist

Related on AIssential

• From Raw Text to Machine Intelligence: A Complete NLP Pipeline Guide — NLP pipelines convert raw human language into structured numerical representations for machine learning models through sequential processing.
• Understanding TF-IDF: A Simple Guide to a Powerful Text Analysis Technique — TF-IDF (Term Frequency–Inverse Document Frequency) is a fundamental text analysis technique used to quantify the importance of a word in a document relative to a collection of documents, crucial for a…
• The Ultimate NLP Roadmaps; From text Preprocessing to Word Embeddings. — A structured NLP roadmap progresses from text cleaning and vectorization to advanced neural network-based word embeddings.
• Text Preprocessing in NLP: Bag of Words (BoW) and TF-IDF — Bag of Words and TF-IDF are foundational text vectorization techniques essential for converting human language into machine-readable numerical features in NLP.
• Adapting Multilingual Embedding Models to Turkish via Cross-Lingual Tokenizer Surgery and Offline Distillation — Efficient adaptation of multilingual embedding models to specific languages is achievable through tokenizer surgery and offline distillation.
• From TF-IDF to Transformers: Implementing Four Generations of Semantic Search — Semantic search evolved from explicit rules to learned contextual representations, balancing interpretability with semantic depth.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by NLP on Medium.