Category Archives: APIs

Posts about Meaningcloud’s APIs.

Migrate from Textalytics: Spellchecker and Language Analysis API

We have just published and updated in MeaningCloud two of the functionalities that were still pending to migrate from Textalytics.

  • Automatic text proofreading checks spelling, grammar and style in your texts for several languages: Spanisn, English, French and Italian.
  • Full language analysis including lemmatization, Part of Speech tagging and syntactic analysis also for several languages. For this API besides English, Spanish, French and Italian we have also Portuguese and Catalan available.

Textalytics users can access MeaningCloud using the same email and password they already had. If you do not remember your password, you can reset and generate a new password.

Developers using Textalytics’ Spell, Grammar and Style Proofreading API or Lemmatization, POS and Parsing API

If you are a user of the following functionalities and want to migrate to MeaningCloud, you can do it already. You only have to:

  1. Update the access point, since the request and response format does not change. Both HTTP and HTTPS endpoints are available.
    API Textalytics MeaningCloud
    Spell, Grammar and Style Proofreading 
    https://textalytics.com/core/stilus-1.2
    		 
    http://api.meaningcloud.com/stilus-1.2
    Lemmatization, POS and Parsing API 
    http://textalytics.com/core/parser-1.2
    		 
    http://api.meaningcloud.com/parser-1.2
  2. Check your license key in MeaningCloud and make sure that you use the correct (and only) license as the value of the parameter ‘license key’ on all requests. You can copy your license key either from the Licenses section in the Account menu, or from the developers home.

As always, if you have doubts or find any other problem, do not hesitate to write us to support@meaningcloud.com. Nevertheless, in order to ensure a smooth transition for client applications all the Textalytics’ API endpoints will be operational until June 1st, 2015.

Textalytics users: how to migrate your application to MeaningCloud

Textalytics users can access MeaningCloud using the same email and password they already had. If you do not remember your password, you can reset and generate a new password.

Meaningcloud’s API authentication as well as accounting have been simplified. It now requires a single license key for all APIs and  consumption is accounted in number of requests. In order to ensure a smooth transition for client applications all the Textalytics’ API endpoints will be operational until June 1st, 2015.

Developers that use the APIs of Textalytics

If you are a user of the following functionalities and want to migrate to MeaningCloud, you can do it already. You only have to:

  1. Update the access point, since the request and response format does not change. Both HTTP and HTTPS endpoints are available.
    API Textalytics MeaningCloud
    Sentiment Analysis 
    http://textalytics.com/core/sentiment-1.2
    		 
    http://api.meaningcloud.com/sentiment-1.2
    Topics Extraction 
    http://textalytics.com/core/topics-1.2
    		 
    http://api.meaningcloud.com/topics-1.2
    Text Classification 
    http://textalytics.com/core/class-1.1
    		 
    http://api.meaningcloud.com/class-1.1
    Language Identification 
    http://textalytics.com/core/lang-1.1
    		 
    http://api.meaningcloud.com/lang-1.1
  2. Check your license key in MeaningCloud and make sure that you use the correct (and only) license as the value of the parameter ‘license key’ on all requests. You can copy your license key either from the Licenses section in the Account menu, or from the developers home.

For the users of the remaining APIs, you will be informed over the next few weeks.

Users of the Textalytics Add-in for Excel

If you use the Textalytics add-in for Excel and want to upgrade to MeaningCloud:

  1. Uninstall the Textalytics Add-in for Excel.
    1. Open Control Panel > Programs > Programs and Features.
    2. Select the Textalitics add-in for Excel from the list of programs and click the Uninstall button
  2. Download the new version of MeaningCloud add-in for Excel which already contains the updated access points.
  3. Install the new version.
  4. Configure your license key to start analyzing texts.

New release of MeaningCloud APIs

We keep improving the functionality and user experience of MeaningCloud, our horizontal APIs, to offer you a more powerful and easier to integrate text analytics technology.

In this new release, besides incorporating many of the suggestions you have been sending us through our online support, we included several improvements:

  • We extended the possibility of using custom dictionaries to the Sentiment Analysis and the Spell, Grammar and Style Proofreading APIs
  • We optimized and uniformized the user experience and the documentation across the various APIs. 

Improvements to our horizontal text analytics API

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Use MeaningCloud API with the GATE plug-in

In our attempt to make MeaningCloud API the easiest way to use semantics in your application, today we are proud to present our latest development, a MeaningCloud plug-in for GATE.

GATE (General Architecture for Text Engineering) is an open-source workbench for text engineering that makes use of any kind of language processing component, from document crawling to search, and intelligent semantic annotations in particular.

Benefits for GATE and MeaningCloud API users

The plug-in provides GATE users a new set of multilingual functionalities, from parsing to entity extraction and sentiment analysis. For MeaningCloud users it would mean an easier and quicker method to prototype full applications including crawling, post-processing or indexing on annotated documents.  Besides, if you’re familiar with JAPE rules, it would enable to post-process, mix and match annotations from different processing resources for more complex pipelines. Finally, GATE is ideal for sharing and evaluating pipelines between team members, which increases productivity and produces more accurate results.

Fork MeaningCloud SDKs on GitHub!

Here at MeaningCloud we love Git.

If you have read our posts on sentiment analysis (document-level, feature-level), you’ll have seen that we have started to use gists from Github to share our examples and pieces of code in this blog.

Our aim in MeaningCloud is to make the building of semantics into your applications as easy as possible. Besides our public API, we have developed SDKs to make your life easier. Right now, they are the easiest way to start using our Media Analysis and Semantic Publishing APIs.

MeaningCloud API provides SDK for several languages- now available on Github

Would you like to get your hands really dirty? We have published the code in Github recently!

See: https://github.com/TailorBrands/meaning-cloud

 

Analyzing the Voice of the Customer channels at the Sentiment Analysis Symposium

Sentiment Analysis Symposium 2014

A few days ago we did a presentation at the Sentiment Analysis Symposium of New York. In our talk, we explained how to use text analysis technologies to listen to the different Voice of the Customer channels and get customer insights.

Textalytics at Sentiment Analysis Symposium 2014

For companies is vital to understand the opinions that their actual and potential customers express in new channels that are much more spontaneous and less structured than the traditional surveys (e.g. answers in questionnaires, interactions with contact centers, conversations in social media). The reach, the immediacy and the “emotional“aspect of these channels make them an impressive source of raw materials for obtaining valuable insights.

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Tutorial for feature-level sentiment analysis

Heads up!

This tutorial was made for Textalytics and as such, it has become obsolete. You can read the updated version for MeaningCloud in this post.

MeaningCloud provides an API to carry out advanced opinion mining, Sentiment Analysis, which extracts both a global aggregated polarity of the text and a more in-depth analysis, giving a sentence-level breakdown of the polarity, extracting entities and concepts and the sentiment associated to each one of them.

Cover for Marvel's Black Widow #1

Marvel’s Black Widow #1

What makes MeaningCloud Sentiment Analysis API different is the possibility of defining entities and concepts for each call of the API, allowing you to obtain the same detailed sentiment analysis for entities or concepts specific to the domain of your application.

We are going to use comic book reviews to learn how to use this feature, as it’s a very rich domain in which it’s easy to illustrate how useful user-defined concepts and entities can be. This applies either to this field or to others where sentiment comes into play, such as hotel reviews, Foursquare tips, Facebook status updates or tweets about a specific event.

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Sentiment Analysis tool for your brand in 10 minutes!

Have you ever tried to understand the buzz around your brand in social networks? Simple metrics about the amount of friends or followers may matter, but what are they are actually saying? How do you extract insights from all those comments? At MeaningCloud, we are planning a series of tutorials to show you how you could use text analytics monitor your brand’s health.

Today, we will talk about the fanciest feature: Sentiment Analysis. We will build a simple tool using Python to measure the sentiment about a brand in Twitter. The key ingredient is MeaningCloud Media Analysis API which will help to detect the sentiment in a tweet. We will also use Twitter Search API to retrieve tweets and the library matplotlib to chart the results.

Brand monitoring

Listening to what customers say on social networks about brands and competitors has become paramount for every kind of enterprise. Whether your purpose is marketing, product research or public relations, the understanding of sentiment, the perception and the topics related to your brand would provide you valuable insights.  This is the purpose of MeaningCloud Media Analysis API, make easier the extraction of these insights from the myriad of comments that are potentially talking about a brand. This tutorial will guide you through the process of building an application that listens to Twitter for your brand keywords and extract the related sentiment.
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Our new Semantic Publishing API is now available in MeaningCloud

This API allows you to produce and publish more valuable contents, more quickly and at lower costs

UPDATE: this API has been discontinued. Use instead our Solution for Semantic Publishing, featuring APIs like Topics Extraction, Text Classification and Automatic Summarization.

At MeaningCloud we keep developing our roadmap and offering new vertical APIs, optimized for different industries and applications. We are pleased to announce that our Semantic Publishing solutions include a new API, designed especially for media, publishers and content providers in general.

It is a logical step for us, since at MeaningCloud we have been collaborating for years with the most significant enterprises in these industries (PRISA, Unidad Editorial, Vocento, RTVE, lainformacion.com, etc.) and this is one of the markets where we are detecting more demand and where our solutions are gaining more traction.

The Semantic Publishing API incorporates the know-how we have been developing when working with these large companies and packages it in the form of semantic resources, process pipelines and specific configurations for the most common applications and scenarios of this industry: archive management, content generation, customization of information products, etc.

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Recognizing entities in a text: not as easy as you might think!

Entities recognition: the engineering problem

As in every engineering endeavor, when you face the problem of automating the identification of entities (proper names: people, places, organizations, etc.) mentioned in a particular text, you should look for the right balance between quality (in terms of precision and recall) and cost from the perspective of your goals. You may be tempted to compile a simple list of such entities and apply simple but straightforward pattern matching techniques to identify a predefined set of entities appearing “literally” in a particular piece of news, in a tweet or in a (transcribed) phone call. If this solution is enough for your purposes (you can achieve high precision at the cost of a low recall), it is clear that quality was not among your priorities. However… What if you can add a bit of excellence to your solution without technological burden for… free? If you are interested in this proposition, skip the following detailed technological discussion and go directly to the final section by clicking here.

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