Category Archives: MeaningCloud

This category groups the different aspects of MeaningCloud we talk about in the blog.

Automatic IAB tagging enables semantic ad targeting

Our Text Classification API supports IAB’s standard contextual taxonomy, enabling content tagging in compliance with this model in large volumes and with great speed, and easing the participation in the new online advertising ecosystem. The result is the impression of ads in the most appropriate context, with higher performance and brand protection for advertisers.

What is IAB’s contextual classification and what is it good for

The IAB QAG contextual taxonomy was initially developed by the Interactive Advertising Bureau (IAB) as the center of its Quality Assurance Guidelines program, whose aim was to promote the advertised brands’ safety, assuring advertisers that their ads would not appear in a context of inappropriate content. The QAG program provided certification opportunities for all kinds of agents in the digital advertising value chain, from ad networks and exchanges to publishers, supply-side platforms (SSPs), demand-side platforms (DSPs), and agency trading desks (ATDs).

The Quality Assurance Guidelines serve as a self-regulation framework to guarantee advertisers that their brands are safe, enhance the advertisers’ control over the placement and context of their ads, and offers transparency to the marketplace by standardizing the information flowing among agents. All this, by providing a clear, common language that describes the characteristics of the advertising inventory and the transactions across the advertising value chain.

Essentially, the contextual taxonomy serves to tag content and is made of standard Tiers, 1 and 2 – specifying, respectively, the general category of the content and a set of subcategories nested under this main category – and a third Tier (or more) that can be defined by each organization. The following pictures represent those standard tiers.
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Join MeaningCloud at the 2016 Sentiment Analysis Symposium

Banner Sentiment Analysis SymposiumMeaningCloud is excited to be sponsoring the 2016 Sentiment Analysis Symposium, taking place July 12 in New York. Join us there!

The Symposium is the first and best conference to address the business value of sentiment, opinion, and emotion in social, online, and enterprise data. The audience is comprised of business analysts, developers, data scientists, and researchers, applying text, sentiment, and social analytics to a host of business challenges. And the speakers? They represent users like Johnson & Johnson, the Mayo Clinic, and VML, analysts like Forrester Research, and innovative start-ups and established technology players.

We will present MeaningCloud’s text and sentiment analysis technology during the symposium program, and you can meet us for a personalized demo in the SAS16 exhibit area or for an informal chat during symposium networking breaks.

If you’re up for a deep technical introduction, start your Symposium experience with an optional half-day tutorial — Computing Sentiment, Emotion, and Personality — taught July 11.

There’s good reason the Symposium has been going strong since 2010. Come network and learn with some of the best sentiment and social data innovators around. Use the registration code MEANING to save 20% on your ticket — register online here — and we’ll see you in New York!

Sentiment Analysis in Excel: optimizing for your domain

In previous tutorials about Sentiment Analysis and our Excel add-in, we showed you step by step how to carry out a sentiment analysis with an example spreadsheet. In the first tutorial we focused in how to do the analysis, and then we took a look at the global polarity we obtained. In the second tutorial, we showed you how to customize the aspect-based sentiment analysis to detect exactly what you want in a text through the use of user dictionaries.

In this tutorial we are going to show you how to adapt the sentiment analysis to your own subdomain using of our brand new sentiment model customization functionality.

We are going to continue to use the same example as in the previous tutorials, as well as refer to some of the concepts we explain there, so we recommend to check them out beforehand, specially if you are new to our Excel add-in. You can download here the Excel spreadsheet with the data we are going to use.

The data we have been working on are restaurant reviews extracted from Yelp, more specifically reviews on Japanese restaurants in London.

In the last tutorial, we saw that some of the results we obtained could be improved. The issue in these cases was that certain expressions do not have the same polarity when we are talking about food or a restaurant than when we are using them in a general context. A clear example of this is the verb ‘share’. It is generally considered something positive, but in restaurant reviews it’s mostly mentioned when people order food to share, which has little to do with the sentiment expressed in the review.

This is where the sentiment model customization functionality helps us: it allows us to add our own criteria to the sentiment analysis.

Let’s see how to do this!
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Sentiment Analysis in Excel: customizing aspect-based analyses

In the previous tutorial we published about Sentiment Analysis and MeaningCloud’s Excel add-in, we showed you step by step how to do a sentiment analysis using an example spreadsheet. Then we showed you a possible analysis you could obtain with its global polarity results.

In this tutorial we are going a bit further: instead of analyzing the global polarity obtained for different texts, we are going to focus on the analysis of different aspects that appear in them and how to use our dictionaries customization console to improve them and to extract easily the exact information you are interested in.

We are going to work withe same example as before: reviews for Japanese restaurants in London extracted from Yelp. If you don’t have it already from the previous tutorial, you can download the spreadsheet with the data here.

If you followed the previous tutorial, you will remember that when you run the sentiment analysis without changing its default settings, two new sheets appear: Global Sentiment Analysis and Topics Sentiment Analysis. Topics Sentiment Analysis shows you the concepts and entities detected in each one of the texts and the sentiment analysis associated to each one of them.

But what can we do when these are not the aspects of the text we are interested in analyzing? This is where our customization tools come in. Our dictionaries customization console allows you to create a dictionary with any of the concepts or entities you want to detect in your analysis, down the type you want them to have associated.

So how do we create this user dictionary?
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A tailored sentiment analysis (recorded webinar)

Last May 4th we presented our webinar “An entirely tailored sentiment analysis using MeaningCloud”. Thank you all for your attendance.

After a brief introduction to MeaningCloud and the operation of its add-in for Excel, we developed a practical example of sentiment analysis in a specific domain (restaurant reviews) and showed how MeaningCloud’s customization tools can be used to improve the accuracy of the analysis:

  • By including attributes that are relevant to the domain and focusing the analysis around them, through the creation of personal dictionaries of entities and concepts.
  • By specifying the polarity of expressions in the domain depending on the context, thanks to the definition of personal sentiment models.

Together, these tools enable our users to be greatly autonomous in the customization of MeaningCloud and put the highest-quality sentiment analysis at everybody’s fingertips.

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Sentiment Analysis in Excel: getting started

Excel spreadsheets are one of the most extended ways of working with big collections of data. They are very powerful and they are very easy to combine and integrate with a myriad of other tools. Through our Excel Add-in we provide you a way of adding MeaningCloud’s analyses to your work pipeline. It’s very simple and it has the added benefit of not needing to write any code to do it.

In this tutorial we are going to show you how to use our Excel Add-in to do sentiment analysis. We are going to do so by analyzing restaurant reviews we’ve extracted from Yelp.

To get started, first you need to register in MeaningCloud (if you haven’t already), and download and install the Excel add-in in your computer. Here you can read a detailed step by step of the process.

Once you’ve installed it, a new tab called MeaningCloud should appear when you open Excel. If you click on it, these are the buttons you will see.

excel add-in ribbon

The first thing you need to do to start using the add-in is to copy your license key and paste it on the corresponding field in the settings menu. You will only have to do this the first time you use the add-in, so if you have already used it, you can skip this step.

Once the license key is saved, you are ready to start analyzing!
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Language Technology Industry meets in Brussels, May 16-17, 2016

Language Technology Industry Summit

Logo_LTi_2016

LT-Innovate, the Language Technology Industry Association, organizes a new edition of its annual Summit. It is the yearly point of convergence between the Language Technology Industry, its clients, research partners and policy makers. According to its Memorandum of Association (London, 2012), LT-Innovate is a non-governmental organization consisting of all parties involved in the field of Language Technologies (LT) and services. Main goals are the promotion of common interests in the successful development, production, delivery and use of language technologies and services and the implementation of services that may help to promote the industry.

LTI Cloud

Besides traditional sections, as Solution Showcases, Technology Spotlights, and Project Results, Language Technology Industry Summit 2016 will serve as the official launch of one of the most important endeavours undertaken by the Association since its inception: the LTI Cloud.

LTI Cloud is the one-stop-shop platform for making available, discovering, assembling, testing and prototyping language technology components. If you are a potential provider of LT APIs (researcher, developer, startup…), and you want to get exposure, testing, or simply customers, consider using LTI Cloud, as it is a ready-to-use platform.

Still in a pilot phase until May 17th, you can be among the first adopters of LTI Cloud. And remember that it serves not only LT providers but also final users. Jochen Hummel, the leader of this initiative, will make the presentation at the Conference. By now, you can take a look at this preview.

Coming back to the Summit, I would like to stress a traditional track: “Customers challenge the Industry”. This year’s challenge comes from Elsevier: “Dynamic Knowledge Stores and Machine Translation”. It will be presented by Michelle Gregory and Pascal Coupet.

MeaningCloud User Profiling API

Being MeaningCloud one of the founding companies of LT-Innovate, we are proud to take an active role again in this year’s event. On Tuesday 17th of May, I will be presenting our recent work on “Automatic Extraction of Rich Customer Profiles from their Activity in Social Networks”. It is about our brand new MeaningCloud API for automatic profiling of Twitter users. User Profiling API allows extracting some important demographics according to different aspects for a given Twitter user: which topics the user talks about, personal and professional information, hobbies and interests, etc. This information extraction is based on a mixed rule-based and machine learning approaches.

Conference Discount Code

Come and join us at the LT-Innovate Summit. And, before registration, do not forget to ask for a special discount code through our helpdesk (support@meaningcloud.com).

A sentiment analysis entirely tailored to your needs with our new customization tool

The adaptation to the domain is what makes the difference between a good sentiment analysis and an exceptional one. Until now, the possibilities of adapting MeaningCloud’s sentiment analysis to your domain relied on the use of personal dictionaries – to create new entities and concepts that the Sentiment Analysis API employed to carry out its aspect-based analysis – or you had to ask our Professional Services Department to develop a tailor-made sentiment model.

Sentiment Models buttonWith the release of Sentiment Analysis 2.1, we incorporated a new customization tool designed to facilitate the creation of personal sentiment models. This tool fully employs our Natural Language Processing technology to enable you to be autonomous and develop —without programming— powerful sentiment analysis engines tailored to your needs.

Other tools for customizing sentiment analysis available on the market, mostly permit to define “bags of words” with either positive or negative polarity. Our tools go far beyond and enable you to:

  • Define the role of a word as a polarity vector (container, negator, modifier), allowing to use lemmas to easily incorporate all the possible variants of each word
  • Specify particular cases of a word’s polarity, depending on the context in which it appears or its morphosyntactic function in each case
  • Define multiword expressions as priority elements in the evaluation of polarity
  • Manage how these custom polarity models complement or replace the general dictionaries of every language.

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Sentiment Analysis 2.1: Migration guide

We have released a new version of our sentiment analysis API, Sentiment Analysis. In Sentiment Analysis 2.1:

  • We’ve changed how the sentiment model is sent in order to enable the use of custom sentiment models across all the APIs that support sentiment analysis.
  • Support to analyze documents and URLs has been added.
  • A configurable interface language has been added to improve multilingual analyses.

As you would see, this is a minor version upgrade, so the migration process will be fast and painless. In this post, we explain what you need to know to migrate your applications from Sentiment Analysis 2.0 to Sentiment Analysis 2.1.
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Voice of the Customer in the insurance industry

For insurance companies, it is vital to listen and understand the feedback that their current and potential customers express through all kinds of channels and touch points. All this valuable information is known as the Voice of the Customer.  By the way, we had already dedicated a blog post to Text mining in the Insurance industry.

(This post is a based upon the presentation given by Meaning Cloud at the First Congress of Big Data in the Spanish Insurance Industry organized by ICEA. We have embedded our PPT below).  

More and more insurance companies have come to realize that, as achieving product differentiation at the industry is not easy at all, succeeding takes getting satisfied customers.

Listening, understanding and acting on what customers are telling us about their experience with our company is directly related to improving the user experience and, as a result, the profitability. In the post on Voice of the Customer and NPS, we saw in more detail this correlation between customer experience and benefits.

 

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