Author Archives: César De Pablo

About César De Pablo

Senior software engineer at MeaningCloud. I felt in love with language fifteen years ago. Since then, learning every day a bit about natural and artificial languages, big data and building ideas into software.

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
    Lemmatization, POS and Parsing API
  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 Nevertheless, in order to ensure a smooth transition for client applications all the Textalytics’ API endpoints will be operational until June 1st, 2015.

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!



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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Semantic Analysis and Big Data to understand Social TV

We recently participated in the Big Data Spain conference with a talk entitled “Real time semantic search engine for social TV streams”. This talk describes our ongoing experiments on social media analytics and combines our most recent developments on using semantic analysis on social networks and dealing with real-time streams of data.

Social TV, which exploded with the use of social networks while watching TV programs is a growing and exciting phenomenon. Twitter reported that more than a third of their firehose in the primetime is discussing TV (at least in the UK) while Facebook claimed 5 times more comments behind his private wall. Recently Facebook also started to offer hashtags and the Keywords Insight API for selected partners as a mean to offer aggregated statistics on Social TV conversations inside the wall.

As more users have turned into social networks to comment with friends and other viewers, broadcasters have looked into ways to be part of the conversation. They use official hashtags, let actors and anchors to tweet live and even start to offer companion apps with social share functionalities.

While the concept of socializing around TV is not new, the possibility to measure and distill the information around these interactions opens up brand new possibilities for users, broadcasters and brands alike.  Interest of users already fueled Social TV as it fulfills their need to start conversations with friends, other viewers and the aired program. Chatter around TV programs may help to recommend other programs or to serve contextually relevant information about actors, characters or whatever appears in TV.  Moreover, better ways to access and organize public conversations will drive new users into a TV program and engage current ones.

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