Category Archives: Text Analytics

Post that discuss text analytics technology.

How Artificial Intelligence makes RPA smarter: two use cases

RPA-automation-computer-robot-tools and statistics

Artificial Intelligence and RPA

Many organizations could be gaining huge operational efficiencies if they combined Artificial Intelligence and RPA (Robotic Process Automation).

In a previous post (The leading role of Natural Language Processing in Robotic Process Automation) we introduced the subject of NLP in RPA. In this post, we are seeing two use cases where Natural Language Processing (also known as Text Analytics) integrated with RPA/BPM software suites, is mature enough to solve typical insight extraction problems, conveniently and cost-effectively.

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Web scraping and text analytics

Text analytics projects are often dependent on Internet-based public sources such as the World Wide Web. These projects usually begin by extracting data from a variety of websites. We call this process “web scraping” (or “web harvesting”). While users can handle web scraping manually, the term often refers to automated methods executed utilizing a web crawler.

Examples of projects that offer a valuable wealth of information include customer experience (in the same way as patient experience or employee experience), dynamic pricing and revenue optimization, competitor monitoring, or compliance checking. Continue reading →

People Analytics: MeaningCloud book on Amazon!

People Analytics. Data and Text Analytics for Human Resources

People Analytics. Data and Text Analytics for Human Resources. This MeaningCloud book is available on Amazon.

In People Analytics, and in this book, we use the evidence that the data provides to respond to several questions:

  • Which candidate will be high-performing, effective, loyal, and aligned with the corporate culture?
  • How can we measure the economic impact of a training program?
  • How can I segment the workforce to make their actions more effective?
  • Which people are considering leaving the organization?
  • What net benefit will employees contribute throughout time in a particular position?
  • How does employee commitment affect productivity and economic outcomes?
  • How can I design a study that is statistically and mathematically valid?

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Contact center: 6 ways to leverage text and speech analytics

Contact center. Ilustration

At contact centers, text analytics technology provides an unprecedented opportunity to convert customer interactions into business opportunities. We can improve customer experience, boost sales, reduce customer churn and streamline the efficiency of the processes.

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Text analytics explained: MeaningCloud in Italian

In previous posts we spoke about text analysis performed in French and Portuguese. Today we’re wrapping up this linguistics series by discussing the analyses that can be done with Italian texts.

Italian is spoken in several European countries such as Italy, San Marino and Switzerland, totaling almost 70 million speakers. As Italians have migrated all over the world, its language is also present on the other side of the pond. In South America, for instance, it is the second most spoken language in Argentina. In the US, even though it is not an officially spoken language, many of its citizens are of Italian descendent and thus speak the language at home. We wanted to include such a widely spread language in our Standard Languages Pack.

Hello in many languages

Similarly to our previous posts, we are going to explain, in a linguistically-inclined way, what Text Analytics is and which functionalities MeaningCloud provides in Italian.

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TASS 2018: Fostering Research on Semantic Analysis in Spanish

MeaningCloud and University of Jaen have been the organizers of TASS, the Workshop on Semantic Analysis in Spanish language at SEPLN (International Conference of the Spanish Society for Natural Language Processing), again in 2018.

TASS logo

During the years, the research has extended to other tasks related to the processing of the semantics of texts that attempt to further improve natural language understanding systems. Apart from sentiment analysis, other tasks attracting the interest of the research community are stance classification, negation handling, rumor identification, fake news identification, open information extraction, argumentation mining, classification of semantic relations, and question answering of non-factoid questions, to name a few.

TASS 2018 was the 7th event of the series and was held in conjunction with the 34rd International Conference of the Spanish Society for Natural Language Processing, in Seville (Spain), on September 18th, 2018. Four research tasks were proposed. MeaningCloud sponsored this edition with prizes for the best systems in each of the tasks. A comprehensive description paper is (to be) published in Procesamiento del Lenguaje Natural journal, vol 62: TASS 2018: The Strength of Deep Learning in Language Understanding Tasks.

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Text analytics explained: MeaningCloud in Portuguese

A few weeks ago we talked about MeaningCloud’s text analytics performance on French texts. Now it’s Portuguese time!

Portuguese, together with Spanish, has an enormous presence in South America. It is spoken by more than 200 million people in Brazil alone. Not only does it have an immense influence on the economy in South America but throughout Europe too, where it is used by more than 10 million speakers. Africa also has Portuguese-speakers. Angola, which has a population of more than 24 million people, recognizes Portuguese as their official language. Its presence in these three continents makes it hard to miss in our Standard Languages Pack. At MeaningCloud, we offer two Portuguese varieties: Brazilian Portuguese and European Portuguese.

Hello in many languages

Whether the concept “Text Analytics” sounds rather hazy or you are looking for something more specifically language-related, this post is for you. We keep in mind the language diversity and we want to show you all the functionalities we provide in Portuguese.

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Text analytics explained: MeaningCloud in French

Due to the rise of Natural Language Processing technologies, Text Analytics is on everyone’s lips. However, most services in this field are provided in English and, depending on the language you are interested in, it can become difficult to find the functionality you are looking for.

No worries. French, for instance, is a language not only used in all the five continents and with almost 300 million of speakers, but is also either the first or the second language of communication in many international organizations [1]. No wonder why we have it as a part of our Standard Languages Pack!

Hello in many languages

Whether the concept “Text Analytics” sounds rather hazy or you are looking for something more specifically language-related, this post is for you. We keep in mind the language diversity and we want to show you all the functionalities we provide in French.

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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]