In the field of text analytics, aside from the development of categorization models, the application of machine learning (and more specifically, deep learning) has proved to be very helpful for supporting our teams in the process of building/improving rule-based models.
This post analyzes some of the applications of machine/deep learning for NLP tasks, beyond machine/deep learning itself, that are used to approach different scenarios in projects for our customers.
While it is obvious that the priority during this pandemic is to cure the sick, to prevent new cases from surfacing and to ensure there are economic and social measures in place to help the people and businesses most afflicted overcome the current situation; without a doubt, in the near future, the analysis of content related to the coronavirus that has been generated by the media and social network users will be the object of research for numerous disciplines such as sociology, philology, linguistics, audio-visual communication, and politics, to name a few.
At MeaningCloud we want to do our bit in this area, by applying our experience and our Text Analytics solutions to analyze the enormous volume of information in natural language, in Spanish and in other languages, in Spain and in other countries, given that, unfortunately, this is a global crisis.
This first article in the series centers on the thematic analysis of content that has been generated in Spanish by digital media platforms in Spain over the last month, how it has evolved during this period of time and the informative positioning of the main media platforms in Spain.
These other articles (only available, at the moment, in Spanish) analyse conversation topics on Twitter in Spain (both from the hashtags and general topics perspective and also applying a specific thematic categorization) and the linguistic analysis of presidential speeches related to this crisis.
The contact center is a crucial component of the customer experience and increasingly incorporates more channels based on unstructured information. In this post we analyze how advanced semantic analysis can be used to get the most out of the contact center of the future.
Interest in the contact center has multiplied by its greater role as an essential component of the customer experience. New interaction channels (bots, chats, social) add to the traditional email and telephone and enable innovative ways to connect with clients in both inbound and outbound contact centers, both internal to companies of all types and in those operated by BPO vendors to provide outsourced services.
In this way, the contact center (traditionally known as call center) has ceased to be a cost center to become a tool for proactively communicating with and understanding the market, for multichannel business development and for generating value for the company.
Continue reading
One of the most common and extensively studied knowledge extraction task is text categorization. Frequently customers ask how we evaluate the quality of the output of our categorization models, especially in scenarios where each document may belong to several categories.
The idea is to be able to keep track of changes in the continuous improvement cycle of models and know if those changes have been for good or bad, to commit or reject them.
This post gives answer to this question describing the metrics that we commonly adopt for model quality assessment, depending on the categorization scenario that we are facing.
This post presents MeaningCloud’s vision on the state of Natural Language Processing technology by the end of 2019, based on our work with customers and research projects.
NLP technology has practically achieved human quality (or even better) in many different tasks, mainly based on advances in machine learning/deep learning techniques, which allow to make use of large sets of training data to build language models, but also due to the improvement in core text processing engines and the availability of semantic knowledge databases.
Everybody has heard about fake news. Fake news is a neologism that can be formally defined as a type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional print and broadcast news media or online social media. It is also commonly used to refer to fabricated or junk news, with no basis in fact, but presented as being factually accurate.
The reason for putting someone’s efforts in creating fake news is mainly to cause financial, political or reputational damage to people, companies or organizations, using sensationalist, dishonest, or outright fabricated headlines to increase readership and dissemination among readers using viralization. In addition, clickbait stories, a special type of fake news, earn direct advertising revenue from this activity.
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.
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.
We continue with the #ILovePolitics series of tutorials! We will show how to use MeaningCloud for extracting interesting insights to build your own Political Intel Reports and, at the same price, turning you into a Data Scientist giant in the field of Social Media Analytics.
Political issues
Our research objective is to study and compare the discourse of different politicians during the electoral campaign, using their messages in Twitter. We are going to compare tweets by the four most popular (mentioned) politicians in our previous tutorial: Barack Obama (@barackobama), Hillary Clinton (@HillaryClinton), Donald Trump (@realDonaldTrump) and Jeb Bush (@JebBush).
Before we start, three remarks: 1) we will focus on U.S. Politics, in English language, but the same analysis can be adapted for your own country or language as long as it is supported in MeaningCloud, 2) this is a technical tutorial: we will develop some coding, but in general, everyone can understand the purpose of this tutorial, and 3) although this tutorial will use PHP, any non-rookie programmer can translate the programs to any language.
In the last decade, sentiment analysis (SA), also known as opinion mining, has attracted an increasing interest. It is a hard challenge for language technologies, and achieving good results is much more difficult than some people think. The task of automatically classifying a text written in a natural language into a positive or negative feeling, opinion or subjectivity (Pang and Lee, 2008), is sometimes so complicated that even different human annotators disagree on the classification to be assigned to a given text. Personal interpretation by an individual is different from others, and this is also affected by cultural factors and each person’s experience. And the shorter the text, and the worse written, the more difficult the task becomes, as in the case of messages on social networks like Twitter or Facebook.
If you love politics, regardless of your party or political orientation, you may know that election periods are exciting moments and having good information is a must to increase the fun. This is why you follow the news, watch or listen to political analysis programs on TV or radio, read surveys or compare different points of view from one or the other side.
American politics
Starting with this, we are publishing a series of tutorials where we will show how to use MeaningCloud for extracting interesting political insights to build your own political intel reports. MeaningCloud provides useful capabilities for extracting meaning from multilingual content in a simple and efficient way. Combining API calls with open source libraries in your favorite programming language is so easy and powerful at the same time that will awaken for sure the Political Data Scientist hidden inside of you. Be warned!
Our research objective is to analyze mentions to people, places, or entities in general in the Politics section of different news media. We will try to carry out an analysis that can answer the following questions:
This is a technical tutorial in which we will develop some coding. However, we will try to guide you through the whole process, so everyone can follow the explanations and understand the purpose of the tutorial.
For the sake of generality and better understanding, we will focus on U.S. Politics in English, but obviously you can easily adapt the same analysis for your own country or (MeaningCloud supported) language.
And last but not least, this tutorial will use PHP as programming language for the code examples. However, any non-rookie programmer should be able to translate the scripts into any language of their choice.
2024 © All Rights Reserved | Data protection policy | Terms and conditions | Data Processing Agreement MeaningCloud is a trademark by MeaningCloud LLC
