Category Archives: Customization

This category describes MeaningCloud’s cutomization engine.

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 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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Feature-level sentiment analysis

Back when we were called Textalytics, we published a tutorial that showed how to carry out feature-level sentiment analysis for a specific domain: comic book reviews.

Cover for Marvel's Black Widow #1

Marvel’s Black Widow #1

Since then, besides changing our name, we have improved our Sentiment Analysis API and how to customize the different analyses through our customization engine. In this post we are going to show you how to do a feature-level sentiment analysis using MeaningCloud.

One of the main changes in the latest release of our API is the possibility of using custom dictionaries in the detailed sentiment analysis provided by the Sentiment Analysis API. We are going to use comic book reviews to illustrate how to work, but the same process applies to any other fields where sentiment comes into play, such as hotel reviews, Foursquare tips, Facebook status updates or tweets about a specific event.

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Exploring Social Media for Healthcare Data

People enjoy sharing information through social media, including healthcare data. Yeah, it is true! And it constitutes the starting point of the research work titled ‘Exploring Spanish health social media for detecting drug effects’, which aims at following social media conversations to identify how people talk about their relation with drug consumption. This allows identifying possible adverse effects previously unknown related to these drugs. Although there is a protocol to communicate to the authorities the identification of a drug adverse effect, only a 5 – 20% of them are reported. Besides, conversations around drugs, symptoms, conditions and diseases can be analyzed to learn more about them. For example, it is possible to see how people search for specific drugs using social media, while others sell them, perhaps illegally. Many others talk about mixing alcohol with drugs or other illegal substances. Of course, one cannot believe everything that appears on the Internet —this is another issue—, but it can highlight some hypothesis for further research.

drugs

Some researchers from the Advanced Databases Group at Carlos III University of Madrid have carried out the mentioned study, designing hybrid models to capture the needed knowledge to identify adverse effects. The Natural Language Processing platform which supports the implementation of the analysis process based on such models is MeaningCloud. The customization capabilities provided by the platform have been decisive to include specific vocabulary and medical domain knowledge. As we know, the names of drugs and symptoms might be complex and, in some cases, difficult to write properly. The algorithm’s results are promising, with a 10% increase in recall when compared to other known algorithms. You can find further details in the scientific paper published by the BMC Medical Informatics and Decision Making Journal.

These developments have been part of the TrendMiner project, and are now available in the prototype website TrendMiner Health Analytics Dashboard, which shows people’s comments about antidepressants gathered from social media. The console displays the mentions of antidepressants and related symptoms and, by clicking on any of them, their evolution over time. Moreover, the source texts analyzed to compute those mentions are shown at the bottom, with labels highlighting the names of drugs, symptoms or diseases, and any relations among them. Such relations might say if a drug is indicated for a symptom or if a disease is an adverse effect of the mentioned drug. The prototype also allows searching by the ATC code (Anatomical Therapeutic Chemical Classification System) and the corresponding level according to this classification scheme. So, if you mark the ‘By Active Substance’ selector, you are searching any drug containing the active substance of the product you inserted in the search box. Furthermore, the predictive search functionality makes easier to find the right expression for a drug or disease.

Health and pharma companies can exploit their unstructured information

There are new kinds of data that are specific to the healthcare and pharmaceutical industries (such as electronic health records) as well as data science tools that allow us to extract valuable knowledge from that data.

 

With MeaningCloud, it is possible to identify the costs of medical treatments, their efficiency (cost, benefits, and risks), references to drugs, side effects, or long-term results. That is why our text analytics solution for the healthcare and pharma domains has so much potential.


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Sentiment Analysis 2.0: Migration guide

We have released a new version of one of our more popular APIs: Sentiment Analysis. In Sentiment Analysis 2.0:

  • The rules used for defining polarity terms have been greatly improved, adding new operators and making the models used much more flexible, which in turn leads to better results.
  • Sentiment analysis is now done at more levels, allowing to identify more complex syntactic structures and to obtain more detailed information about how the polarity is expressed.
  • More configuration options have been added related to the morphosyntactic analysis over which the sentiment analysis is carried out.
  • The architecture of the service has changed, leading to a tenfold improvement in the response time.
  • An integration with the Lemmatization, PoS and Parsing API has been added in order to ease the way of creating applications that use the information provided by both APIs.
  • Dictionary customization has been fully integrated in order to get out the most out of its functionality.

All these improvements mean the migration process is not as fast as it would be with a minor version. These are the things you need to know to migrate your applications from Sentiment Analysis 1.2 to Sentiment Analysis 2.0.
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Customize your text analytics tools (recorded webinar)

Last May 14th we presented our “Boost your text analytics accuracy” webinar.

We discussed why we need to customize text analytics processes -by including domain-based information- to improve the accuracy (precision, recall) of these tools. And we did a walk-through of MeaningCloud’s features to customize several of its functions:

  • Text classification
  • Information extraction
  • Sentiment analysis.

These customization tools feature graphical user interfaces and are very easy to use, thus empowering the users to adapt the system to their applicactions and putting high-quality text analytics at everybody’s fingertips. We are confident that these features, together, are unique in the industry and put MeaningCloud ahead of the competitors’ offerings.

For those of you interested, below you can find the webinar’s slides and recording.

(También presentamos este webinar en español. Tenéis la grabación aquí.)

MeaningCloud Webinar – Better Text Analytics Using Customization Tools from MeaningCloud on Vimeo.

Boost your text analytics accuracy with our customization tools (webinar)

Text analytics tools are extraordinarily valuable for extracting meaning from unstructured content, but the use of generic linguistic resources limits their accuracy. For instance, an automatic system will never identify a company’s products if these haven’t previously input into the tool’s dictionaries.

Text Analytics CustomizationThe inclusion of application-specific linguistic resources (dictionaries, models) allows these tools to reach high levels of precision and recall, but, in general, this is an expensive process that requires a deep proficiency in these technologies.

At MeaningCloud we aim at democratizing semantic analysis, putting it at every user’s and developer’s fingertips. This is why MeaningCloud features Customizer, a personalization engine based on graphical tools that enable users to create their own classification models and dictionaries in a simple, interactive way. Therefore, anybody needing, for example, to analyze comments about hotels in London can include the establishments, attributes and vocabulary used in this context.

Register for this free MeaningCloud webinar and discover in a practical way how Customizer tool empowers you to perform domain-oriented text analytics with total autonomy, flexibility and maximum accuracy.

UPDATE: this webinar has already passed. See the documentation and recording here: