Text mining tool
Text mining is the process of extracting structured, useful information from unstructured text using computational methods, keywords, recurring themes, named entities, and sentiment, pulled out of reviews, comments, survey answers, or documents. Paste a block of text into the free tool below and see what it finds, no setup required.
This tool applies core text mining techniques, tokenization, keyword extraction, and sentiment scoring, to any text you paste, up to a set character limit. No account needed for a single run. Longer or recurring analysis, hundreds of reviews at once, benefits from a dedicated workflow rather than a one-off paste, covered in the FAQ below.
Reviews, comments, survey responses, support tickets, or an article. Nothing leaves your browser and no account is required.
The top keywords ranked by frequency, recurring topics with the exact phrases behind them, named entities such as brands or people, and an overall sentiment breakdown. Load the sample data to see a full run.
Top keywords
Recurring topics
Named entities
Gather reviews, comments, transcripts, articles, or support tickets into one place. The tool below works on anything you paste, one pass at a time.
Remove duplicates, stray HTML, emojis if they are not useful signal, and fix any obvious encoding issues before you mine it.
Split the text into words and phrases, drop stop words, and normalize case so "Great" and "great" count as the same signal.
Pull out keywords, key phrases, named entities such as people, products, and places, and the recurring topics that show up more than once.
Run sentiment analysis, topic modeling, or classification depending on what decision the output needs to support.
Look at frequency, topic clusters, and the sentiment split rather than reading every line in order.
Feed the ranked topics and phrases into a report, a set of themes, or directly into a creative brief or messaging decision.
Text mining is the process of extracting structured, useful information from unstructured text using computational methods. It is easy to confuse with adjacent terms. Natural Language Processing, or NLP, is the broader field of techniques, parsing, tokenization, embeddings, that text mining applies to a specific corpus. Data mining works on structured, tabular data, spreadsheets and databases, while text mining works on unstructured text: reviews, transcripts, articles, tickets. Text analytics is often used interchangeably with text mining, though "analytics" sometimes leans toward business reporting and dashboards, where "mining" leans toward extraction.
The category is not small. The global text analytics market was valued at USD 18.81 billion in 2026 and is projected to reach USD 51.17 billion by 2031, a 22.16% CAGR, per Mordor Intelligence. That growth is driven by organizations that already own more customer text, reviews, support tickets, survey answers, than anyone has time to read by hand.
Pulling out the most important terms in a body of text. If "shipping delay" surfaces repeatedly across support tickets, that phrase is the keyword worth acting on, not a generic word count.
Identifying products, brands, people, and locations in text. NER is what turns "Acme Corp mentioned three times" from a manual read into an automatic count.
Grouping large volumes of text into underlying themes without manual tagging, useful when there are thousands of documents and no time to read them all.
Classifying text as positive, negative, or neutral, sometimes with an intensity score. Useful as a first pass, though it can misread sarcasm or nuance, which is why the phrases behind a score matter more than the score alone.
Sorting text into predefined buckets, like support ticket types, and cleaning the input first: removing duplicates, normalizing case, stripping stray HTML.
Labeling each word by its grammatical role, noun, verb, adjective, which downstream techniques like NER and topic modeling rely on to work correctly.
Compressing a long document or a large batch of reviews into a short summary that keeps the load-bearing points and drops the repetition.
Extraction is only half the job. A ranked list of a thousand keywords is not more readable than the original text unless something turns it into a shape a person can scan in seconds. Word clouds are the simplest version: word size mapped to frequency, useful for a quick first look though weak on nuance since they flatten context. Voyant Tools goes further, a free browser-based corpus reader built for exactly this: word frequency trends over a document, keyword-in-context views, and collocation graphs that show which words tend to appear near each other.
Network and relationship diagrams map how entities and topics connect, which brand gets mentioned alongside which complaint, useful once a dataset is large enough that simple frequency stops explaining the pattern. Dashboards sit on top of all of this: a live view a team can filter by date, topic, or sentiment rather than a static export that goes stale the day after it is generated. The tool on this page renders its output as ranked cards rather than a chart, because for a single pasted batch, a scannable list beats a visualization built for a dataset ten times the size.
The right tool depends on volume, technical skill on the team, and whether the job is a one-off or a recurring workflow.
| Dimension | Free browser tools | Code libraries (NLTK, spaCy) | Enterprise platforms (NVivo, WordStat, Alteryx) |
|---|---|---|---|
| Setup and skill required | None. Paste text, get output, no install. | Python or R, plus time to write and maintain a pipeline. | Vendor onboarding, often a license and a dedicated analyst. |
| Volume it handles well | A single batch, one paste at a time. | From hundreds to millions of documents, scales with the code. | Large qualitative or survey datasets, built for repeat runs. |
| Customization | Fixed technique set, no code to change. | Full control over every step of the pipeline. | Configurable within the vendor's framework and taxonomy tools. |
| Typical user | Anyone doing a one-off check on a review export or an article. | Data scientists and engineers building a recurring pipeline. | Academic researchers and market research or CX teams. |
| Cost | Free. | Free and open source, engineering time is the real cost. | Paid license, often per seat or per project. |
NLTK and spaCy are the two most widely used open-source NLP libraries for Python; NVivo, WordStat, and Alteryx are commercial platforms aimed at research and enterprise analytics teams respectively.
Finding recurring complaints, praise, or feature requests across reviews, comments, and survey responses. Typical input: a review export. Typical output: a ranked list of what to fix or what to lead an ad with.
Scanning public text, forums, social posts, articles, for trends. Typical input: scraped or exported public posts. Typical output: emerging themes worth acting on before a competitor does.
Mining literature for patterns across thousands of papers. Typical input: an abstract or full-text corpus. Typical output: recurring methods, findings, or gaps across a field.
E-discovery and contract analysis. Typical input: a document set under review. Typical output: flagged clauses or entities that need human sign-off.
Mining clinical notes for patterns. Typical input: de-identified clinical text. Typical output: recurring symptoms or treatment patterns worth a closer look.
Brand sentiment and emerging topics. Typical input: public posts and comments mentioning a brand. Typical output: a sentiment trend line and the specific phrases driving it.
Start with data sources: can it ingest the text you actually have, CSV exports, review platforms, spreadsheets, APIs? Then check technique coverage: does it support the specific analysis you need, sentiment, topic modeling, entity extraction? Language support matters if you operate outside English-only markets. Output format decides how usable the result is: raw scores, a dashboard, or an actionable summary you can hand to a team. Technical skill required splits the market into no-code tools and Python or R libraries for teams with engineering resources. Volume and scale matter too, a tool that works on 50 rows may not hold up on 50,000. Generalist tools are fine for one-off analysis, but a team mining reviews and comments every week for marketing decisions usually needs something built for a recurring workflow rather than a one-time script, which is the gap a purpose-built platform like Selzee is built to close for ecommerce teams specifically.
"The reviews sitting in a Shopify store and the comments under last month's best ad are the highest-signal text most brands never mine. Text mining is not about reading faster, it is about reading at all."
If the text worth mining is specifically customer reviews, comments, and feedback, and the goal is faster, sharper marketing decisions rather than general research, that is a narrower job than generic text mining. That is also the case teams spend the least time on, since marketers activate just 33% of their martech stack's capabilities, down from 42% in 2022, per Gartner. Selzee is built for that specific case: turning reviews, comments, and campaign performance into hooks, briefs, and creative direction for ecommerce teams, without the manual digging. Try the AI for Advertising tool to go straight from pasted reviews to ad hook directions, or the creative brief generator to turn a theme into a full brief.
No. Many no-code tools, including the one on this page, handle extraction and analysis in the browser. Custom pipelines built with Python or R libraries like NLTK and spaCy offer more control, but they are only needed once the job is a recurring pipeline rather than a one-off check.
Accuracy depends on text quality, language complexity, and the technique used. Sentiment analysis in particular can misread sarcasm or nuance, which is why the quoted phrases behind a theme matter more than a single sentiment score on its own.
The terms are largely interchangeable. "Mining" emphasizes extraction, pulling structured signal out of raw text, while "analysis" emphasizes interpretation, deciding what that signal means for a decision.
It depends entirely on the tool or model. Many free, browser-based tools are English-only; enterprise platforms and code libraries like spaCy support a wider range of languages, so check coverage before relying on one for a non-English dataset.
Plain text, CSV exports, PDFs with OCR or parsing, and API-pulled text are all common formats. The free tool on this page works on plain pasted text, so a CSV or PDF export needs to be opened and copied in first.
Yes. Several open-source libraries, NLTK and spaCy among them, and browser-based tools including the one above work well for smaller, one-off jobs. Recurring or large-scale needs usually call for a paid or purpose-built tool instead.
Credit-based and simple: $150/mo for individuals, $300/mo for SMBs, and $1,500/mo for bigger teams, with the same features on every plan. New signups get 2,000 free credits, no card required, and the text mining tool on this page is free with no account at all.
The tool above shows what one paste of text produces. Selzee does it continuously across your reviews, comments, Shopify data, and Meta Ads performance, and hands your team the hooks, angles, and briefs worth building next.
See how Selzee mines customer feedback for your team, Request a demoKeep exploring: AI for advertising, the free ad analysis tool, creative brief templates, creative AI apps.