Category Archives: MeaningCloud

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

MeaningCloud sponsors the award for Author Profiling Research at PAN also in 2018

Author Profiling and Text Forensics Research

CLEF Conference 2018Since 2009 the PAN Lab organizes shared tasks on digital text forensics in general, and in author profiling in particular. Pan Lab is part of CLEF, the European Conference and Evaluation Forum around Information Retrieval. CLEF consists of an independent peer-reviewed conference on a broad range of issues in the field of multilingual and multimodal information access evaluation, and a set of labs and workshops designed to test different aspects of mono and cross-language information retrieval systems. CLEF 2018 will be hosted by the University of Avignon, France, 10-14 September 2018.

MeaningCloud has been sponsoring the award to the best performing team in the author profiling task at CLEF since 2015.

Author profiling is a task that given a document has the aim to infer what are the traits of its author.
In 2017 the task focused on gender and language variety identification in Twitter addressing four languages and several of their varieties: English (Australia, Canada, Great Britain, Ireland, New Zealand, United States), Spanish (Argentina, Chile, Colombia, Mexico, Peru, Spain, Venezuela), Portuguese (Brazil, Portugal), and Arabic (Egypt, Gulf, Levantine, Maghrebi).

Paolo Rosso delivers the 2017 PAN Author Profiling Price to the team of University of Groningen

Paolo Rosso delivers the 2017 PAN Price to the team of University of Groningen

Twenty-two were the participating teams from all over the world in 2017 and the best results were obtained by Angelo Basile, Gareth Dwyer, Maria Medvedeva, Josine Rawee, Hessel Haagsma, and Malvina Nissim, from the University of Groningen, The Netherlands.

This year the task will go multimodal and not only textual information in tweets will be taken into account but also images of URLs will be used as information sources in order to infer gender demographics. Three will be the languages that will be addressed: English, Spanish and Arabic [http://pan.webis.de/clef18/pan18-web/author-profiling.html].

Paolo Rosso
Universitat Politècnica de València, Spain
Co-organizer of the author profiling task at PAN

References

Rangel F., Rosso P., Potthast M., Stein B. (2017). Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In: Cappellato L., Ferro N., Goeuriot L, Mandl T. (Eds.) CLEF 2017 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org, vol. 1866. [http://ceur-ws.org/Vol-1866/invited_paper_11.pdf]

Potthast M., Rangel F., Tschuggnall M., Stamatatos E., Rosso P., Stein B. (2017). Overview of PAN’17: Author Identification, Author Profiling, and Author Obfuscation. In: 8th Int. Conf. of CLEF on Experimental IR Meets Multilinguality, Multimodality, and Visualization, CLEF 2017,
Springer-Verlag, LNCS(10456), pp. 275–290 [http://www.uni-weimar.de/medien/webis/publications/papers/stein_2017k.pdf]

MeaningCloud participates in the first Global Legal Hackathon

global legal hackaton

The first phase of the first Global Legal Hackathon (GLH) was held February 23-25, 2018. David Fisher, organizer of the event and founder of the technological and legal company Integra Ledger, estimates that the GLH will have a great impact. He hasn’t spoken too soon; global participation in the GLH nearly matched that of an earlier event organized by NASA, and it has been considered the largest hackathon organized to date. For 54 hours, more than 40 cities across six continents participated simultaneously. The teams were made up of engineers, jurists, lawyers, and people in business who all worked toward a common goal: to lay the foundations for legal projects that can improve legal work or access to legal information through an app, program, or software. Continue reading →

Vertical packs: trial and subscription

We recently published our first two vertical packs: Voice of the Customer and Voice of the Employee, both with several Deep Categorization models in English and Spanish.

We are happy to announce that both packs are now available for automatic subscriptions. In the same way you can choose which plan you want to subscribe depending on the credits, rate limit and resources you need, all the public packs are now included in the upgrade process.

Select packs

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The Text Proofreading API moves to Stilus

Even since the very beginnings of MeaningCloud, we have offered a Text Proofreading API in Spanish which allow you to standardize and ensure the quality of your contents through spelling, grammar and style proofreading.

Stilus logo

On the 2nd of April, we will definitely move this API and its functionality to Stilus, an application where we take advantage of the functionality provided by the API and show everything you can do with it.

To those of you who currently use it, the migration process can be done in three easy steps:

  1. Register at Stilus.
  2. Contact us at support telling us about your volume requirements and which Stilus user would use the API. We will inform you of the conditions and tell you how to subscribe to the API.
  3. Once you have subscribed, you will only have to change the API endpoint and the key parameter value in your integration, and you will be all set to keep using the Text Proofreading API.

If you’d rather use directly the text proofreading functionality online or from Word, check out all the ways in which you can use Stilus!

Updated version of the IAB model in the Deep Categorization API

IAB - Interactive Advertising Bureau

The Interactive Advertising Bureau (IAB) is perhaps the most influential organization in the online advertising business and, currently, brings together more than 650 leading companies in the industry that control 86% of the U.S. market. With a strong presence in the rest of the industrialized world as well, today IAB has become a standard for content classification, especially in fields with strong ties to the digital economy and new social media.

In fact, IAB promotes advertising techniques like behavioral targeting, which allows advertisers to direct marketing campaigns to specific users (according to their age, place of residence, political views, interests, etc.) and thus increase their effectiveness. What’s more, the organization is making consistent progress in the field of geotargeting, an area of digital marketing that is on the rise thanks to the unprecedented diffusion of mobile devices connected to the Internet and the latest advances in Internet-of-things technologies. Continue reading →

Applying text analytics to financial compliance

In one of our previous posts we talked about Financial Compliance, FinTech and its relation to Text Analytics. We also showed the need for normalized facts for mining text in search of suspects of financial crimes and proposed the form SVO (subject, verb, object) to do so.

financial crime

Financial crime

Thus, we had defined clause as the string within the sentence capable to convey an autonomous fact. Finally, we had explained how to integrate with the Lemmatization, PoS and Parsing API in order to get a fully syntactic and semantic enriched JSON-formatted tree for input text, from which we will work extracting SVO clauses.

In this post, we are going to continue with the extraction process, seeing in detail how to work to extract those clauses from the response returned by the Parsing API.

Continue reading →

How to build a Financial Compliance model ready for FinTech

What is Financial Compliance and what is FinTech?

financial crime

Financial crime

Financial crime has increasingly become of concern to governments throughout the world. The emergence of vast regulatory environments furthered the degree of compliance expected even from other non-governmental organizations that conduct financial transactions with consumers, including credit card companies, banks, credit unions, payday loan companies, and mortgage companies.

Technology has helped financial services address the increased burden of compliance in innovative ways which have also yielded other benefits, including improved decision-making, better risk management, and an enhanced user experience for the consumer or investor.

The rapid development and employment of AI (Artificial Intelligence) techniques within this specific domain have the potential to transform the financial services industry.

FinTech (Financial Technology) solutions have recently arised as the new applications, processes, products, or business models in the financial services industry, composed of one or more complementary financial services and provided as an end-to-end process via the Internet. You can find additional interesting information in this article.

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Voice of the Employee Dashboard

Voice of the Employee gathers the needs, wishes, hopes, and preferences of all employees within an organization. The VoE takes into account both explicit needs, such as salaries, career, health, and retirement, as well as tacit needs such as job satisfaction and the respect of co-workers and supervisors. This post follows the line of Voice of the Customer in Excel: creating a dashboard. We are creating another dashboard, this time for the Voice of the Employee.

Text-based data sources are a key factor for any organization that wants to understand the “whys”.

Continue reading →

Recorded webinar: Vertical Packs, VoC, VoE

Thank you all for your interest in our webinar “MeaningCloud Vertical Packs: the Fastest Way to Benefit from Text Analytics” that we delivered last December 20th, where we explained how to customize text analytics with only one click  and we presented our  Packs for the analysis of the Voice of the Customer and the Voice of the Employee.

During the session we covered these items:

  • Introduction to text analytics and MeaningCloud.
  • Why Vertical Packs? How they create value.
  • What are the components of Vertical Packs: models, APIs, integrations.
  • Available Packs: Voice of the Customer, Voice of the Employee.
  • Case study: analysis of the Voice of the Customer.
  • Coming developments: product roadmap

IMPORTANT: this article is a tutorial based on the demonstration that we delived and that includes the data to analyze and the results of the analysis.

Interested? Here you have the presentation and the recording of the webinar.

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

Voice of the Customer in Excel: creating a dashboard

Excel spreadsheets are still one of the most extended ways of working with big collections of data, especially among non-technical users. Two of our Vertical Packs, Voice of the Customer and Voice of the Employee, are particularly useful for typically non-technical teams, which can now carry out their analyses easily with our last Excel integration.

In this tutorial, we are going to show you how to use the add-in provided in the Voice of the Customer Vertical Pack, how to carry out a VoC analysis, and how to work with its output by creating a dashboard like the one on the right. Working with the Voice of the Employee Pack would follow a similar pattern.

[This post was last updated in February 2019 to include the updated ontology.]

dashboard general

A practical case

Let us imagine we work for a market research department or agency interested in analyzing the Insurance industry. Customer comments in forums and social networks constitute an extremely valuable source of spontaneous information about their opinions about insurance providers.
We are going to focus specifically on auto insurance reviews extracted from ConsumerAffairs, a website that collects reviews from several domains.

The reviews we are going to use have been extracted from the top five companies in the Auto Insurance section: for each one of them we’ve picked ten items. You can download here the Excel spreadsheet we will be working on. It contains a single sheet where we have included two columns: one with the selected reviews, and another with the name of the company they refer to.

As we have mentioned, for this tutorial we are going to use our Vertical Pack for Voice of the Customer analysis. Vertical Packs are a combination of preconfigured models or dictionaries, powerful APIs and specific add-ins for Excel that enable you to adapt text analytics to your domain with only one click. Just by registering at MeaningCloud, you have a 30-day trial for all Vertical Packs available. The trial starts the moment you first analyze a text, so users that have been using MeaningCloud for a while will also be able to try it out.

To get started, you need to register at MeaningCloud (if you haven’t already), request access to the Voice of the Customer pack and download and install the VoC Excel add-in on your computer. Here you can read a detailed step by step guide to the process.

Continue reading →