Count how often each word appears across a CSV text column
Count word frequency across a text column in a CSV and output a frequency table.
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Why Onfyt: Reports top bigrams (two-word phrases) alongside individual word counts—useful for identifying product-category pairs or common complaint phrases.
Interactive Sample Data
| id | product_name |
|---|---|
| 1 | Solar Panel 250W Monocrystalline |
| 2 | Solar Panel 300W Polycrystalline |
| 3 | Solar Inverter 1500W Pure Sine |
How this tool works
Counting how often each word appears in a text column answers practical questions without requiring a full NLP pipeline: which product categories appear most in a catalog export, which complaint keywords dominate a support ticket column, which hashtags recur most in a social media export. The output is a two-column frequency table—term and count—sorted descending, ready to sort, filter, and paste into a dashboard. DataFix tokenizes text by whitespace and punctuation, applies optional case normalisation (so 'Solar' and 'solar' count together), and optionally filters out common English stopwords to surface meaningful terms. Analyzing word frequency across text reviews, feedback forms, and survey comments provides instant semantic insight into customer sentiment and common keyword patterns without complex natural language tooling. Word frequency analysis extracts key vocabulary and recurring customer sentiment themes from customer reviews, feedback forms, and support ticket transcripts. Our tokenization engine filters common stop words, normalizes character casing, and computes exact occurrence counts to surface dominant keyword patterns. Word frequency tokenization strips common punctuation while preserving domain-specific contractions, hyphenated technical terms, and numeric codes. This granularity ensures that technical logs and user reviews yield actionable frequency distributions. Analyzing word occurrences across customer reviews and support tickets highlights recurring customer sentiment and dominant topics. Automated tokenization strips common stopwords to surface high-signal keywords rapidly. Collapsing casing differences ensures words like "Invoice" and "invoice" contribute to the same summary count in text frequency analysis.
Things that usually go wrong
- Words with punctuation attached ('data.', 'data,', 'data') are counted as different words unless punctuation stripping is enabled.
- Stop words ('the', 'a', 'is') inflate the frequency table — enable stop word removal.
Edge cases we tested
🧪 Column containing numbers and units ('250W', '300Ah')
Treated as tokens; reported in frequency table unless stopword-filtered.
🧪 Multi-language text column
Tokenized by whitespace; language-specific stopwords not applied unless selected.
Frequently Asked Questions
Are words lowercased before counting?
Yes by default — 'Data' and 'data' are counted together.
Can I analyse N-grams (phrases)?
Bigrams and trigrams are supported — select the N-gram size in the options.
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