Скачать или смотреть Understanding Positive and Negative Word Frequencies in NLP

Learn what a frequency dictionary is and how positive and negative word frequencies are used in NLP for tasks like sentiment analysis, text classification, and topic modeling.
Want to understand how sentiment analysis works behind the scenes?
In this video, you’ll learn how to create a frequency dictionary in Natural Language Processing (NLP) — a simple yet powerful technique used to analyze positive and negative word frequencies in text data.

We’ll walk through:

What a frequency dictionary is and why it’s important

How to count positive and negative word occurrences

How to use these counts for sentiment classification

A beginner-friendly Python demo using tweets or short sentences

By the end, you’ll know how to transform text into numerical features that can train machine learning models like Logistic Regression or Naive Bayes for sentiment prediction.

🧠 What You’ll Learn

✅ What frequency dictionaries are in NLP
✅ How to calculate positive & negative word frequencies
✅ Python implementation using real text examples
✅ How to prepare data for machine learning sentiment models

🧩 Python Topics Covered

Tokenization and preprocessing
Counting word frequencies
Building (word, class) mappings
Positive vs negative sentiment representation
Feature extraction for classification
🧾 Code Repository

🔗 Get the complete code here:
👉 GitHub – NLP Projects by Dr. Mushtaq

🔗 Related Videos
🎬 Introduction to Sentiment Analysis with Python
🎬 Bag of Words vs TF-IDF Explained
🎬 Building a Logistic Regression Sentiment Classifier
🕒 Timestamps

00:00 – Introduction
01:15 – What is a Frequency Dictionary?
03:20 – Positive and Negative Word Frequencies Explained
06:00 – Python Implementation
09:30 – Visualizing the Results
11:00 – Summary & Next Steps
#nlp tutorial #sentiment analysis python #text classification #frequency dictionary #positive negative word frequencies #nlp project #machine learning nlp #python sentiment analysis #innovitechs