Ujeebu Article Extraction API extracts clean text, and other structured data from news and blog articles.
Sentiment analysis, stemming and lemmatization, part-of-speech tagging and chunking, phrase extraction and named entity recognition.
One API for all your text analysis needs. Sentiment Analysis, Topic Tagging, Lemmatizer, and much more. Various NLP tools all with one plan. Use natural language processing to analyze and understand human sentences.
TextAnalysis API provides customized Text Analysis,Text Mining and Text Processing Services like Text Summarization, Language Detection, Text Classification, Sentiment Analysis, Word Tokenize, Part-of-Speech(POS) Tagging, Named Entity Recognition(NER), Stemmer, Lemmatizer, Chunker, Parser, Key Phrase Extraction(Noun Phrase Extraction), Sentence Segmentation(Sentence Boundary Detection), Grammar Checker and other Text Analysis Tasks. It stands on the giant shoulders of NLP Tools, such as NLTK...
Text Analysis APIs streamlines the data mining process for developers and businesses so that they can quickly classify data from a variety of sources.
A text analysis application programming interface (API) enables communication between devices. The code contains documentation that defines how implementations called function calls are carried out. In essence, the documentation tells the API what to do. More specifically, the API looks for information in a group of text from one or more sources. It then analyzes the content and then categorizes the data.
Text analytics APIs return statistical tables or graphs, which is slightly different from an API for text analysis.
These APIs are powered by artificial intelligence (AI). The machine learning technology of Natural Language Processing (NLP) makes it possible for APIs to extract unstructured data automatically. Two terms that are used interchangeably with text analysis are text mining and text extraction. All three have the same meaning.
Classifying unstructured text is the main function of a text analysis API. It does this by assigning categories or preset tags to content. For example, an API might classify the phrase, “It fit my budget!” under “Price.”
These coding tools are for API users and developers who want to create applications that classify and automatically categorize text from a variety of sources. The sources could be from customer feedback, social media posts, emails, surveys, and others.
APIs for text mining enables businesses to recognize the intent of customer feedback, and as a result, they provide a useful way to improve customer service.
Chatbots are good examples of text mining too. An API can classify the text for support and automatically route the customer to the appropriate department.
APIs for text extraction may use sentiment analysis to recognize how text conveys emotion. Businesses bolster their brand by properly handling negative complaints.
Users and developers can expect accurate text extraction and coding flexibility. Businesses benefit by quickly being able to flag certain tickets, reviews, or social media comments.
Open-source libraries such as Python’s NumPy and SciPy are available online. Twinword Text Analysis Bundle API Documentation and its Sentiment Analysis API Documentation are two freemium APIs to try out as well.
All text analysis APIs are supported and made available in multiple developer programming languages and SDKs including:
Just select your preference from any API endpoints page.
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