Exploratory analysis of HuffPost news articles in the News Category dataset, covering class balance, missing values, text lengths, timeline patterns, and dominant terms.
Top news categories ranked by number of articles. The long tail of smaller classes is a key issue for downstream classification.
Headlines are short and compact, usually around 10 words.
Combining headlines with short descriptions adds much more text context for NLP tasks.
Character-length histograms complement word counts and show how compact or verbose the raw text strings actually are.
Character distributions are useful when estimating storage, truncation risk, and input limits for downstream models.
Average combined text length for the largest categories. This highlights which news sections tend to use longer summaries.
Article volume across years. Later years are noticeably smaller, which suggests a temporal skew in the dataset.
Top categories tracked across years to reveal how editorial focus changes over time.
Missing values are concentrated in `short_description` and `authors`, while label and link fields are complete.
Frequent content words reveal a strong concentration of politics, media, and lifestyle language.
This view keeps stopwords instead of removing them, so you can inspect how much of the corpus is made of high-frequency function words.
Bigram frequencies reveal common phrase patterns that simple unigram counts cannot capture.
TF-IDF highlights words that are especially characteristic of each category rather than merely frequent overall.