Microsoft Translator APIs can be seamlessly integrated into your applications, websites, tools, or other solutions to provide multi-language user experiences. Leveraging industry standards, it can be used on any hardware platform and with any operating system to perform language translation and other language-related operations such as text language detection or text to speech. Click Here for more information about the Microsoft Translator API.
English dictionary API providing an access to data of over 800 000 English lexical entries, such as words, phrasal verbs, multiword expressions etc. Use the API to get word definitions, usage examples, pronunciations, synonyms and antonyms or for text processing (lemmatization, morpheme segmentation, root word extraction, word inflections).
RussianSentimentAnalyzer (RSA) is a JSON API based on the technology stack of Insider Solutions company. It is capable of parsing the input text, reconstructing the meaning of messages with typos, like tweets and finding sentiment polarity oriented towards a particular object. Consider an example: I like new GalaxyS, but do not enjoy new iPhone. If there are no objects, the sentiment of this sentence can be detected as NEUTRAL or MIXED. If, however, GalaxyS has been passed in as an object, the sentiment will be POSITIVE. It will be NEGATIVE for iPhone in this particular example. Currently the API supports Russian language with input texts varying from long formal news posts to informal and short tweets. Looking for text analytics APIs? Check the full list here: https://www.mashape.com/dmitrykey
Keyword Extraction API provides professional keyword extractor service which is based on advanced Natural Language Processing and Machine Learning technologies. It can be used to extract topn important keywords from the URL or document that user provided. If you want test our automatic keyword extraction service, you can use our free automatic keyword extractor online demo: http://keywordextraction.net/keyword-extractor
Topics Extraction tags locations, people, companies, dates and many other elements appearing in a text written in Spanish, English, French, Italian, Portuguese or Catalan. This detection process is carried out by combining a number of complex natural language processing techniques that allow to obtain morphological, syntactic and semantic analyses of a text and use them to identify different types of significant elements.
The Document Structure Analysis extracts different sections of a given document with markup content (which includes formatted documents such as PDF or Microsoft Word files), including the title, headings, abstract and parts of an email. This process, even though it takes into account some language markers, is based mainly in the markup of the document, so it can be applied to documents in any language.
Deep Categorization is MeaningCloud's solution for in-depth rule-based categorization. It assigns one or more categories to a text, using a very detailed rule-based language that allows you to identify very specific scenarios and patterns using a combination of morphological, semantic and text rules.
Dandelion API is a set of semantic APIs to extract meaning and insights from texts in several languages (Italian, English, French, German and Portuguese). It's optimized to perform text mining and text analytics for short texts, such as tweets and other social media. Dandelion API extracts entities (such as persons, places and events), categorizes and classifies documents in user-defined categories, augments the text with tags and links to external knowledge graphs and more. Dandelion API easily scales to support billions of queries per day and can be adapted on demand to support custom and user-defined vocabularies. Additional languages are available on demand.
Multilingual sentiment analysis of texts from different sources (blogs, social networks,...). Besides polarity at sentence and global level, Sentiment Analysis uses advanced natural language processing techniques to also detect the polarity associated to both entities and concepts in the text. Sentiment Analysis also gives the user the possibility of detecting the polarity of user-defined entities and concepts, making the service a flexible tool applicable to any kind of scenario. Additionally, Sentiment Analysis detects if the text processed is subjective or objective and if it contains irony marks [beta], both at global and sentence level, giving the user additional information about the reliability of the polarity obtained from the sentiment analysis.
This service provides detailed linguistic information for a given text in English, Spanish, French, Italian, Portuguese and Catalan. There are three operating modes that cover different aspects of the morphosyntactic and semantic analysis: Lemmatization, which provides the lemmas of the different words in a text; PoS tagging: which provides not only the grammatical category of a word, including semantic information about that word; Syntactic analysis: that provides a thorough syntactic analysis, giving a complete syntactic tree where the leaves represent the most basic elements and their morphological and semantic analyses.
Automatic multilingual text classification according to pre-established categories defined in a model. The algorithm used combines statistic classification with rule-based filtering, which allows to obtain a high degree of precision for very different environments. Three models available: IPTC (International Press Telecommunications Council standard), EuroVocs and Corporate Reputation model. Languages covered are Spanish, English, French, Italian, Portuguese and Catalan.
Get your API key at https://api.bitext.com/#/signup/api Contact us at [email protected] You can use this REST API to perform: Sentiment Analysis: Structure every part of an opinion into positive/neutral/negative, identify the topic of the opinion, the sentiment expression used, and get a numeric value for each of the sentiment-bearing phrases. Entity Extraction: Extract from text names, places, firms, twitter users, and others. Categorization: Classify text using a custom build taxonomy Concept Analysis: Linguistically based structuring text.
NLP and text analytics have come a long way. Software and REST APIs today can not only read text but also extract entities, relationships, facts and even detect emotions. Imagine the applications you can build with powerful text analysis. With RapidAPI's text analysis APIs your app can easily implement text mining, text classification, language detection, text comparison, text summarization, sentiment analysis, and entity extraction without any expenditure on machine learning infrastructure.
Text analysis is the automated process of obtaining information from text. It is often used to extract and classify information from text blocks, social media (tweets, facebook), tickets, product info/reviews, surveys, and more. Companies or developers may want to extract information to gain detailed analysis such as sentiment analysis and language detection. Text analysis and natural language processing APIs are crucial in automating this work.
Large companies, data scientists, and developers tend to use text analysis software to extract specific information and structured data (or unstructured) from large pieces of text documents or blocks to gain insights into their text analytics.
Analyzing text is important because it will help save time and provide insights you may not have seen if you're analyzing data manually.
All APIs listed in this collection can be used in your favorite programming language or SDK. Explore all the endpoints and test them individually to get JSON responses.
Check out related tools and cognitive services such as Microsoft, IBM Watson, and other APIs from the Google Cloud Platform.