Author Archives: Antonio Matarranz

About Antonio Matarranz

Chief Marketing Officer at MeaningCloud

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!


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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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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Books Are a Service

Semantic Publishing and Voice of the Customer understanding for the media&content industry

The reason for publishing being a key industry to take advantage of text analytics is also the reason why the industry finds it so hard to engage with the technology.

Books are a serviceThe reason? Text. And a lot of it. The publishing world has struggled to understand how data relates to text and understand the value of data. This is changing, too slow for many, as the industry moves from seeing themselves as a ‘product’ based company (e.g. making books, e-books or physical) to a ‘service’ based company. In other words smart publishers are starting to see their service to customers as the creator and curator of information. This content is abled to be mixed and mashed-up in dynamic ways across a number of formats. This service is not bound, saddle-stitch or otherwise, to a specific product. This 180-degree perspective change requires publishers to think more directly about customer experience in the same way more traditional service based industries like hospitality or even retail banking.

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Text Analytics for Publishing: there’s metadata and smarter metadata

Everyone agrees metadata is great. It helps simplify the management and packaging of content and data. It creates consistency and provenance of your content and data across an organization. Metadata gives you that 35000 feet perspective that is needed to make strategic decisions. This is especially important for publishers whose stock in trade is human language, which is completely opaque to machines whose world consists of zeros and ones. Your customers aren’t calling or emailing you to know what is in such and such database. No. They are contacting you because they want to know what monographs you have by such and such professor or asking you for all the archival material on ‘cats’, ‘World War 2’ or ‘nanotubes’. As a human, you understand exactly what they are looking for. If your ICT has a smidgeon of metadata, you can dig around that such-and-such database and deliver the content and have a happy customer.

Intelligent content for Semantic Publishing

Metadata TagMetadata makes your content more intelligent. That’s why everyone agrees metadata is great. Great until they have to either enter the metadata or maintain the vocabularies. Some organizations are lucky. They have ensured there is support within the workflow and people with the expertise to do the hard work so when that customer searches on the website, they quickly find what they are looking for and go away happy. But, even those lucky few do not live in isolation. There is no publisher of consequence who doesn’t have do deal with 3rd party content and data. A huge amount of additional effort is spent shoehorning 3rd party content into the metadata models of the organization. Every publisher has a workflow that includes completely throwing away existing metadata and spending additional time and wasteful effort to add metadata that their CMS can handle. Does that sound familiar? Does it feel better to know you aren’t the only one?

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MeaningCloud sentiment analysis powers SocialBro’s Twitter platform

The leading social marketing tool vendor applies MeaningCloud’s advanced sentiment analysis to detect the opinion of Twitter users with the highest quality and without having to develop language processing technology.

UPDATE: as of March 2016 SocialBro has been rebranded as Audiense.

SocialBro analyzes over 15 million tweets per month to extract insights that are essential for its clients’ marketing activities and campaigns. And a key ingredient of these insights is the analysis of Twitter users’ sentiment.SocialBro logo

Due to the characteristics of its business, SocialBro had some very demanding requirements in the field of sentiment analysis: a high throughput, great accuracy and the possibility of carrying out aspect-based analyses. Instead of developing its own sentiment analysis technology, SocialBro decided to turn to a specialized supplier to avoid undertaking developments outside its core business. With this aim, they chose MeaningCloud.

MeaningCloud’s Sentiment Analysis API service stands out for its semantic approaches based on advanced natural language processing. It internally employs a syntactic-semantic tree representation of the text on which it deploys the polarity of the different terms. Then, it combines and spreads these polarities according to the morphological category of each term and the syntactic relations among them.

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Some conclusions from our Text Analytics survey

What does “text analytics” mean to you and your organization? How do you plan to use Text Analytics in 2016? For MeaningCloud, as a text analytics tool vendor, having some answers to these questions is key to understand our market and define our product strategy: this was the purpose of the survey we kept open during some weeks, since the beginning of last October.

Even though the number of respondents was quite low (60) it is definitely possible to draw some conclusions and trends that we summarize in this post.

Applications: customer is first

What is your text analytics application scenario? No doubt this is the main question when one needs to analyze the uses of this technology. In our results, Understanding customer attitudes, behaviors, and needs was the most mentioned scenario (62%), followed by Research (48%) and Content Classification, recommendation, and personalization (43%) as it can be seen in the figure. The following two categories were Customer service, improving customer experience (40%) and Brand/reputation management (38%), which means that everything related to customer understanding, improving customer experience, and managing the brand lead the text analytics application area, coping 3 of the 5 first positions.

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New release of MeaningCloud

We have just published a new release of MeaningCloud that affects Topics Extraction, Lemmatization, POS and Parsing, and Text Classification APIs. Although there are several new features in terms of new functionalities and parameters, the most important aspect of this release lies under the hood and essentially consists of a refactoring of the way in which concept-type topics are internally handled, much more in line with the use of other semantic resources. This lays the foundations for better performance and new features related to the extraction of this type of information. Sty tuned for great improvements in this area in future releases.

The other two great lines of this release are the enrichment of the morphosyntactic analysis with information extraction and sentiment analysis elements (which enable new and richer types of analyses that combine the text’s structure with topics and polarity) and a new predefined classification model.

Here are some details about the developments in the different APIs:

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How might your organization employ Text Analytics in 2016?

Help us design the best Text Analytics tool

If you are a MeaningCloud user or are otherwise involved in Content Analytics or Text Mining, we’d like to hear your opinion.

We want to know what “text analytics” means to you and your organization. We are researching current trends and issues in the market, both business- and solution-related, including adoption by industry and business function, successes and failures, and requirements for the software tools of the future.

Please take part in our survey. Respondents will receive a copy of the conclusions.

The survey is at https://www.surveymonkey.com/r/SurveyTextAnalytics

and it’s open till the end of  November 18th.

Take the Survey

Thank you!


What You Need To Know about Text Analytics

You have enough to worry about. You know your industry inside and out. You know your products and services and how they compare with the competition’s strengths and weaknesses. In business, you have to be an expert in a range of topics. What you don’t need to worry about are the ins and outs of every technology, algorithm and software program.

This is especially true of an inherently complex technology such as natural language processing. As a business owner you have enough to worry about. Do you really have time to understand morphological segmentation? Text analytics should be just another tool in your toolbox to achieve your business goals. The only thing you need to know is what problems you have that can be solved by natural language processing. Anaphoric referencing? Don’t worry about it. We have it covered it, along with anything else you might need from language technology.

Text Analytics

What do you do need to know about text analytics?

Text analytics goes by many names: natural language processing (NLP), text analysis, text mining, computational linguistics. There are shades of difference in these terms, but let the expert work that out. What you need to know is that these terms describe a variety of algorithms and technology that is able to process raw text written in a human language (natural language) to provide enriched text. That enrichment could mean a number of things:

  • Categorization – Classifying text according to themes, categories or a taxonomy
  • Topic Extraction – Identifying key named entities and concepts in the text such as people, places, organizations, and brands
  • Sentiment Analysis – Detecting whether the text is talking about those concepts in a positive or negative light

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