Keyword Extractor
Commercial usage OK
579 mamodheru
Hapana watermark
Hapana kumbobvira kushanyira
Model:
+ GPT-5, Claude, Gemini
Advanced options
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Kubvisa mazwi akakosha kubva kune chero tebhu nemahara AI. SEO uye ongororo yemukati yakapusa.
Maitiro ekuisa Keyword Extractor
1
Sarudza yako input
Tinya tebhu, wedzera faira, kana kuti nyora zvaunoda. Hapana account yaunoda.
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Tinya kuumba
Our AI inogadzirisa yako mibvunzo mumasekondi nekushandisa yakanakisa open-source mamodheru.
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Dhawunirodha & shandisa
Dhawunirodha, kopa kana kugovera yako mhinduro. Yemahara yekushandisa personally uye commercially.
Usashandisa iyi chirongwa kuburikidza neAPI
Automatize iyi chirongwa kubva yako pachako code. OpenAI-kuenderana REST endpoint, Bearer-token auth, hapana zvishoma SDK zvinoda. Token mari inoenzana web interface.
curl -X POST https://api.free.ai/v1/chat/ \
-H "Authorization: Bearer sk-free-..." \
-H "Content-Type: application/json" \
-d '{"model": "qwen7b", "messages": [{"role": "user", "content": "Summarize this: ..."}]}'Kubatana Free AI Zvirongwa
Keyword Extractor - FAQ
Kubvisa mazwi uye mazwi akakosha kubva kune chero chinyorwa chenguva refu (chinyorwa, blog post, transcript, chirevo chechigadzirwa). Maitiro mana ekubvisa: (1) SEO mazwi akakosha nekutsvaga-kuedza kutambanudza, (2) nyaya dzepamusoro-soro + pfungwa, (3) zita rezita - vanhu / nzvimbo / sangano, (4) tag-style lowercase-hyphen-joined tags, (5) akateedzana akateedzana mazwi. Maitiro akatora 0-100 neTF-IDF, raw frequency, kana semantic relevance. Kutumira kunze se CSV, TXT, kana JSON.
Yeah - a 1,500-word nyaya zvinobuda mu ~ 350 tokens pa default Qwen 3 30B model, comfortably mukati 6,000 anozivikanwa kana 30,000 anonyora-up zuva nezuva pool. No kushanyira zvinoda kuti yako yekutanga kubuda.
Izvo zvinhu zvinotora data rekutsvaga-data rekutsvaga kwemazwi aunoda kuti uvape ($ 99 + / mo). Iyi chirongwa inoita zvakasiyana - inotora mazwi akakosha evavhoti kubva kune yako saiti uye inoongorora kutsvaga kwavanoda. Workflow: shandisa izvi kuti uwane izvo mazwi ako achangobva kukurudzira, wozoisa iwo muAhrefs / Semrush kuti uone mwedzi wega wega wekutsvaga.
Three scoring modes: (1) TF-IDF (default) anopa mashoko maviri anowanzoitika muichi chinyorwa uye anowanzoitika muChirungu - zvakanaka SEO sezvo izvi zviri kuchinja zvirevo. (2) Raw frequency inofungidzira kuitika straight - zvakanaka kana iwe uchida kuona zvaunoziva kuti mashoko. (3) Semantic relevance scores by proximity to the documents main thesis even if a phrase appears only once - best for topical coherence.
N-gram inotevera N mazwi. "kuchinja kwemamiriro ekunze" inotevera 2-grama. "artificial intelligence model" inotevera 3-grama. Toggle the chips to extract only 1-grams (single words like "photosynthesis"), 2-grams (head phrases like "carbon footprint"), or 3-grams (long-tail like "carbon footprint calculator"). Most SEO workflows want 2+3-grams - single words are too generic.
Ndinoda - default stoplist anobvisa "the", "and", "of", "is", etc. mu 14 mitauro. Unogona kuwedzera custom stopwords kuburikidza "Words to exclude" field - nyore kubvisa yako brand, zvigadzirwa zvinyorwa, kana boilerplate legalese kuti zvinenge zvakaomarara zviwanikwa.
Google inoparadzira mibvunzo nechinangwa chemushandisi: chinopa ruzivo ("X ndechipi"), chinopa ruzivo rwekufambisa ("nike official store"), chinopa mari ("best running shoes 2026"), chinopa mari ("buy air max online"). Chigadzirwa ichi chinoita kuti zvive nyore kuongorora kuti chinyorwa chako chinofanira kuonekwa sei nevatsvakurudzi, vatengesi kana vaverengi vemapeji ewebhu.
99 languages via the underlying Qwen 2.5 model. Highest quality on English, Spanish, French, Portuguese, German, Chinese, Japanese. Lower-source languages work but 3-gram extraction may be less reliable — fall back to 1+2-grams for Arabic, Hindi, Turkish, etc.
The UI runs one text at a time. For bulk extraction wire the /v1/chat/ API into a Python script — iterate over your CMS export, POST each article body, dump the JSON. Bearer auth, 1,000 calls/hour on free accounts, higher limits on pro. Docs at /api/.
RAKE uye YAKE ndezvimwe zvekare zvema algorithms - zvakaoma asi zvakaoma mu semantic nuance (vanoda "tech startup" kana iyo dokumendi ichiti "technology startup" asi kwete "tech" chete). KeyBERT inoshandisa BERT embeddings - yakanaka semantic mhando asi yakaderera uye inodiwa kuiswa. TextRank inoenderana negraph. Isu tinotora nzira yedu inoshandisa 7B-parameter LLM iyo inoita zvese zvakapfuura zero-shot plus search-intent classification - yakaderera pafoni asi zero setup, yemahara, uye hapana ML engineering inodiwa.
Mu frequency uye TF-IDF modes, yes - every keyword is derivable from the source text verbatim or with minimal flection change. In semantic-relevance mode the model may return a controlled-vocabulary version ("pulmonology" even if your text says "lung doctor visit"). The academic-terms mode intentionally prefers controlled vocabulary.
Yeah — POST to /v1/chat/ with the same system prompt this page builds (inspect template source for the exact prompt). Returns structured JSON. Good for content-strategy dashboards or CMS plugins. Bearer auth, monthly limits. Docs at /api/.
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