📰 What Is EDA in News Content?
EDA in news content means exploring articles, headlines, or reports to:
- Understand themes and topics
- Identify trends over time
- Detect bias or sentiment
- Analyze keywords, sources, or categories
🧰 What You Might Explore:
| Feature | Example Questions |
|---|---|
| Word frequency | What are the most common words in political news this week? |
| Publishing trends | Which topics have become more popular over time? |
| Sentiment | Is the tone of coverage mostly positive or negative? |
| Named Entities | Which people, places, or orgs appear most often? |
| Sources | Which news outlets publish most frequently on this topic? |
| Length & readability | Are articles getting shorter or more complex over time? |
🔧 Common EDA Techniques for News Content:
1. Text Cleaning & Preprocessing
- Remove punctuation, stopwords, and lowercase everything
- Tokenization, stemming or lemmatization
2. Word Clouds / Frequency Plots
- Visualize most common words or phrases
3. Topic Modeling (e.g., LDA)
- Discover hidden themes or topics in large corpora
4. Time-Series Analysis
- Track frequency of words or articles by date (e.g., mentions of “climate change” over years)
5. Sentiment Analysis
- Use tools like VADER or TextBlob to gauge tone of coverage
6. Named Entity Recognition (NER)
- Extract and count names of people, places, organizations using NLP tools
📊 Real EDA Questions for a News Dataset:
- What topics were most common during an election year?
- Are certain outlets more negative when covering specific parties?
- How does coverage of war vs. peace topics vary over time?
- Do different regions use different language in reporting similar stories?
🛠️ Tools You Can Use:
- Python libraries: pandas, matplotlib, seaborn, spaCy, NLTK, gensim, wordcloud
- Auto NLP tools: MonkeyLearn, Hugging Face, NewsWhip (for live news tracking)
Would you like a sample dataset or code snippet showing EDA on news headlines or articles?