Keyword and Word Frequency Analysis with Text Cleaning and Preprocessing
Start with what the words say. We clean and prepare your text, count the words and phrases that matter, and show them in context so you understand your data before you model or code it.
What this service covers
- Text cleaning: case, punctuation, numbers, spelling variants and stop words
- Tokenisation, stemming and lemmatisation
- Word and phrase (n-gram) frequencies
- TF-IDF to find words that distinguish groups or documents
- Keyword in context (KWIC) and collocations
- Word clouds and frequency charts
- Comparison across groups, time periods or sources
Why preprocessing matters
Raw text is messy. “Colour”, “color” and “Colours” may count as three words, and common filler words dominate frequency lists. Preprocessing standardises the text so counts reflect meaning. We make choices explicit, for example which stop words were removed, so the analysis can be repeated and defended.
Frequency is a starting point
The most frequent word is not always the most informative. TF-IDF highlights words that are distinctive for a group, and collocations show which words go together. Keyword in context displays each occurrence with its surrounding words so you can see how a term is really used. Try the basics on your own text with our free word frequency counter, keyword in context viewer and word cloud generator.
Where it leads
Word counts often point the way to deeper methods: topic modelling for themes, sentiment analysis for tone, or thematic analysis for interpretation. Cleaned text is also the input for most of our other text services.
Deliverables
Tables of terms, charts, word clouds used with caution and a short interpretation. See also survey and social media text analysis.
How the work runs
- You share the texts and the questions you have about them.
- We agree cleaning rules and run the analysis.
- We review results with example contexts.
- You receive tables, charts and interpretation.
What you receive
- Cleaned text dataset
- Word and n-gram frequency tables
- TF-IDF and collocation results
- KWIC extracts for key terms
- Charts and a short written summary
Frequently asked questions
Should I remove stop words?
Usually for frequency analysis, but not always. For some methods they carry meaning. We explain the choices.
What is lemmatisation?
Reducing words to their dictionary form, so “running” and “ran” count as “run”.
Are word clouds useful?
They give a quick visual impression but are weak for precise comparison. We pair them with tables.
Can you handle misspellings?
We can standardise common variants and spelling errors where they affect results.
Do you support non-English text?
For many languages, yes. Tell us the language.
How large can the text be?
From a few pages to millions of words.
Related nlp & text analytics services
Topic Modelling
Discover themes in large text collections using LDA, NMF or BERTopic.
Sentiment Analysis
Positive, negative and neutral sentiment for reviews, comments and posts.
Survey & Social Media Text Analysis
Themes, sentiment and trends from open-ended answers, posts and reviews.
NLP & Text Analytics Services
See every text analytics service we offer.
Also related: Content analysis