Wordsfinder Extractor API (also known as Keyword Extractor API or Relevant Words Extractor API) is an automated technique that detects the most used and most relevant words and phrases from text data. It is an approach to get the core content of a text and obtain the most popular issues discussed. It uses ML (Machine Learning), AI (Artificial Intelligence) and NLP (Natural Language Processing) to analyze human language. It allows to detect keywords from documents, reports, social media reviews, forums, etc.
Manual extraction and analysis is time and money consuming. With this API you can skim a full data set to extract words that best describe and represent each review, in few seconds. You get immediate output of what consumers most frequently mention. It scours attributes in insights both in mobile and web version. It detects keywords or key phrases within text data.
Zyla Labs´ Wordsfinder Extractor API builds up word or tag clouds to show visualizations of the most often used words in given segments. The bigger words in the cloud are the most used. The word cloud generator in the API assists in extracting representative keywords from text.
Why Is Keyword Extraction Important?
The detected words in massive datasets furnish precious information of what consumers are concerned about. Automation assists in analyzing data more efficiently. Varied issues are reflected in extracted information: price, quality, satisfaction and dissatisfaction, etc. These judgements are instrumental to design business strategies, based on what consumers think of your products and services, and even what they think about your competitors.
No matter what your business activity is, Wordsfinder Extractor API is an essential tool to mark data, analyze a text and produce tag clouds with the most relevant keywords. It is functional and effective, its output is accurate, detection is in real time.
The output assists you in generating adjustments and improvements, in making decisions to optimize quality (product and service). On top of everything, keyword extraction models are easily set up and implemented. The results can consist of basic statistics strategies from counting word frequency, to more sophisticated approaches by opposing to previous models.
The former is one of the most rudimentary methods to spot and extract the main keywords and key phrases in a text set, detecting frequency, collocations and co-occurrences. These methods do not demand expertise, but as they are based on statistics they will certainly discard words mentioned just once but equally representative of the text segment. It counts words and frequency but does not consider meaning, structure, syntax and word sequence.
The latter instead indexes the most repeated words and phrases in a text. This is useful to recognize frequent terms in reviews and to find out the most repeated topics in text data.
How does the keyword extraction algorithm work? It generates keywords and key phrases from text data. It splits input text content by tokens. Then it extracts the most frequent words from the token list, to find the most common ones from the list. Ultimately, an embedding or representation is generated using given models.
Wordsfinder Extractor API makes use of accessory platforms (Relevant words extractor API, Relevant expressions extractor API and Key Chunk extractor API) that make of it a valuable tool for organizations to find out consumers` opinion, sentiment, emotion, approval, disapproval, etc., that guide corporations to improve productivity and generate competitive measures to satisfy the market.