Keyword extraction is a useful tool to obtain relevant information from text data in various sources. Several APIs offer automated versions to analyse text more accurately and faster. They allow users to summarize content and identify key topics being discussed, whether it is in social media sites, documents, meeting minutes, forums, blogs, etc.
If you aim at scouring the internet to find large numbers of comments with judgement and assessment so as to detect the words and phrases that best represent each review, keyword extraction is the most appropriate approach. It allows to visualize the most frequent issues in consumers` insights, and process the information with automated tools, which will save time and money. This software uses NLP (Natural Language Processing) as well as AI (Artificial Intelligence) that generate algorithms and models.
Wordsfinder Extractor API, Relevant Words Extractor API, Relevant Expressions Extractor API, Keyword Extractor API and Key Chunk Extractor API conform a suite of platforms that Zyla Labs makes available to the market of business. They complement one another to render a useful service to clients that are interested in optimizing their productivity and service quality. The consumers` comments are an essential element to gauge how a company is reputed by the community of consumers. Furthermore companies must also consider the public reputation of competitors, as a way to improve and adjust on the basis of what individuals praise and detract.
There are other methods to extract keywords and key phrases from text data. They are alternatives that are also efficient in this process.
RAKE stands for Rapid Automatic Keyword Extraction and is a useful tool to extract relevant information from individual documents. It has multiple applications, it is easy to use and is very successful in scouring, identifying, analyzing occurrence of words, compatibility with other words (co-occurrence) and learning about topics dealt with by reviewers.
YAKE is a method for extracting keywords that is automated, easy to use, without the need of supervision, and based on text features that are drawn from individual documents. The purpose is the identification of the most relevant keywords. It does not require training documents, and is independent of word indexes, text size, theme, language, etc. It classifies few features and combines them to obtain a single score to every keyword.
TEXTRANK is another method of keyword and key phrases extraction based on a graph, in which the edges stand for the connections between words formed by the co-occurrence of words within a given segment. Its algorithm defines the features of the data obtained, ranks words, selects the most representative words and devises adjacent keywords.
KEYBERT is also an accessible keyword extraction algorithm. It generates a representation with models to then draw the most relevant keywords and key phrases. These elements are measured by similarity, and identified as those that best represent the document. The client creates an instance based on the model provided, you select the embedding model (representation) and run the process to obtain the expected result.
All APIs, methods and techniques have their own advantages. Each of them manages to draw keywords as per the user`s request, or close to those, and related to the field of activity. The main advantage is that none of them requires training on external resources. Definitely all of them will bring about benefits for the business: relevant information from the market that will be translated into measures and decisions to improve, adjust and optimize products and services.