Author Archives: Antonio Matarranz

About Antonio Matarranz

Chief Marketing Officer at MeaningCloud

Recorded webinar: Why You Need Deep Semantic Analytics

Last July 13th we delivered our webinar “Why You Need Deep Semantic Analytics”, where we explained how to achieve a deep, automatic understanding of complex documents. Thank you all for your interest.

During the session we covered these items:

  • Automatic understanding of unstructured documents.
  • What is Deep Semantic Analytics? Comparison with conventional text analytics.
  • Where it can be applied.
  • Case study: due diligence process.
  • Ideal features of a Deep Semantic Analytics solution.
  • MeaningCloud Roadmap in Deep Semantic Analytics.

IMPORTANT: you can find a more literary explanation of some of the items we covered, including the due diligence practical case, in this article.

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

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


Deep Semantic Analytics: A Case Study

Scenarios that can benefit from unstructured content analysis are becoming more and more frequent: from industry or company news to processing contracts or medical records. However, as we know, this content does not lend itself to automatic analysis.

Text analytics has come to meet this need, providing powerful tools that allow us to discover topics, mentions, polarity, etc. in free-form text. This ability has made it possible to achieve an initial level of automatic understanding and analysis of unstructured documents, which has empowered a generation of context-sensitive semantic applications in areas such as Voice of the Customer analysis or knowledge management.

Continue reading


Why you need Deep Semantic Analytics (webinar)

Achieve a deep, automated understanding of complex documents

Conventional Text Analytics enable a first level of automatic understanding of unstructured content, achieved through its ability to extract mentions of entities and concepts, assign general categories or identify the polarity of opinions and facts that appear in the text. However, these isolated information elements do not reflect the wealth of information provided by these documents and impose limitations when it comes to finding, relating or analyzing them automatically.

Deep Semantic Analytics represents a step beyond conventional text analytics by providing features such as snippet-level granular categorization, detection of complex patterns, and extraction of semantic relationships between information elements in the document.

Continue reading


RapidMiner: Impact of topics on the sentiment of textual product reviews

This is the second of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. Read the first one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this RapidMiner tutorial we shall attempt to extract a rule set that will predict the positivity/negativity of a review based on MeaningCloud’s topics extraction feature as well as sentiment analysis.

To be more specific, we will try to give an answer to the following question:

  • Which topics have the most impact in a customer review and how do they affect the sentiment of the review that the user has provided?

For this purpose, we will use a dataset of food reviews that comes from Amazon. The dataset can be found here.

Continue reading


Recorded webinar: Integrate the most advanced text analytics into your predictive models

Last April 27th we delivered our webinar “Integrate the most advanced text analytics into your predictive models”, where we presented our new MeaningCloud Extension for RapidMiner. Thank you all for your interest.

During the session we covered these items:

  • Analytics platforms. Introduction to RapidMiner.
  • Text analytics. Introduction to MeaningCloud.
  • Combining text and data analytics. MeaningCloud Extension for RapidMiner.
  • Practical case demo.
  • Application scenarios.
  • How this Extension is different.
  • Product roadmap.

IMPORTANT: The data analyzed during the webinar can be found in this tutorial, along  with the applied RapidMiner workflows and models.

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

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


RapidMiner: Relationship between product scores and text review sentiment

This is the first of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. See the second one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this tutorial we shall analyze a set of food reviews from Amazon. We will use the MeaningCloud sentiment API and try to see how users score products and whether their review description of a certain product corresponds to the score that they have assigned – more specifically we will try to see

  • How closely the review sentiment corresponds to the manually assigned score (which we already have available in our dataset).

The dataset that we will be using throughout the tutorial can be found here. First thing we need to do is download the CSV to our computer.

Continue reading


You can now use MeaningCloud with RapidMiner

Expand text analytics with the tools to create the most sophisticated predictive models

At MeaningCloud, we have just launched a feature that enables users to incorporate our text analytics into complex predictive models based on structured data. With our new Extension for RapidMiner you can directly embed our semantic analysis engines into the process pipelines defined in this popular analytical tool.

RapidMiner is an open-source platform for data science, recognized as a leader in the field of advanced analytics tools. RapidMiner is used for preparing data, creating predictive models, validating them, and embedding them into business processes quickly and easily .

Continue reading


Active social listening to protect Online Reputation (Part 2)

EDITOR’S NOTE: This is a guest post by Leopoldo Martínez D., a researcher and consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

  1. Introduction

As I stated in the first part of this post, I will show how online reputation evaluation was used in a real situation related to the tourism industry.

  1. The unexpected event: A shooting at a music festival in Playa del Carmen, Riviera Maya

January 6-15, 2017, a music festival was scheduled to be held in Playa del Carmen, as well as a series of events related to both music and the tourism industry. On January 15, a shooting occurred in a well-known bar where people were celebrating the end of the festival.

When the shooting happened, messages quickly spread through social networks to give information and comment on the context in which the incident and how it happened. Some conversations revealed an interesting fact: The shooting was not an isolated event but stemmed from the “situation of crime that the Riviera Maya went through in 2011″.

Could this affect the Riviera Maya’s reputation as a tourist destination? Could “several years in a situation of crime” have already influenced the tourism industry’s image? These are some of the questions that the public and private actors that provide services and products in this tourist area might have been asking themselves.

Continue reading


Active social listening to protect Online Reputation (Part 1)

EDITOR’S NOTE: This is a guest post by Leopoldo Martínez D., a researcher, consultant on social media corporate intelligence and lecturer at UCV and IESA (Venezuela), and it was originally published on his blog (in Spanish).

 

1. Introduction

In this previous post, I suggested that conversations taking place in virtual communities fostered by a digital marketing plan generate feedback that is useful for assessing and monitoring a digital’s marketing strategy’s performance.

This feedback could generate a huge amount of valuable data (Big Data) which enables the creation of a knowledge base for the topic being talked about, who is participating, who is having the greatest impact on brand image, products, people, or organizations.

This knowledge base can also be fed by discussions arising from unexpected events which are not part of the communication plan but deal with the virtual community’s topics of interest.

To specifically assess the conversation’s impact, it is necessary to pay attention (beyond listening) to what is being said through metrics (qualitative and quantitative) that reflect the online community’s perception on brands, products, people, or organizations. After all, this perception is a way to measure an online reputation.

With this need in mind, the purpose of this post is to show how to use the active listening of conversations in social networks to evaluate your online reputation.
Continue reading