Tag Archives: text mining

Text Mining

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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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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Text Analytics & MeaningCloud 101

One of the questions we get most often at our helpdesk is how to apply the text analytics functionalities that MeaningCloud provides to specific scenarios.

Users know they want to incorporate text analytics into their processes but are not sure how to translate their business requirements into something they can integrate into their pipeline.

If you add the fact that each provider has a different name for the products they offer to carry out specific text analytics tasks, it becomes difficult not just to get started, but even to know exactly what you need for your scenario.

homer-simpson-confused

In this post, we are going to explain what our different products are used for, the NLP (Natural Language Processing) tasks they are tied to, the added value they provide, and the requirements they fulfill.

[This post was last updated in October 2018 to include our new functionalities.]
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Voice of the Customer in the insurance industry

For insurance companies, it is vital to listen and understand the feedback that their current and potential customers express through all kinds of channels and touch points. All this valuable information is known as the Voice of the Customer.  By the way, we had already dedicated a blog post to Text mining in the Insurance industry.

(This post is a based upon the presentation given by Meaning Cloud at the First Congress of Big Data in the Spanish Insurance Industry organized by ICEA. We have embedded our PPT below).  

More and more insurance companies have come to realize that, as achieving product differentiation at the industry is not easy at all, succeeding takes getting satisfied customers.

Listening, understanding and acting on what customers are telling us about their experience with our company is directly related to improving the user experience and, as a result, the profitability. In the post on Voice of the Customer and NPS, we saw in more detail this correlation between customer experience and benefits.

 

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How might your organization employ Text Analytics in 2016?

Help us design the best Text Analytics tool

If you are a MeaningCloud user or are otherwise involved in Content Analytics or Text Mining, we’d like to hear your opinion.

We want to know what “text analytics” means to you and your organization. We are researching current trends and issues in the market, both business- and solution-related, including adoption by industry and business function, successes and failures, and requirements for the software tools of the future.

Please take part in our survey. Respondents will receive a copy of the conclusions.

The survey is at https://www.surveymonkey.com/r/SurveyTextAnalytics

and it’s open till the end of  November 18th.

Take the Survey

Thank you!

An Introduction to Sentiment Analysis (Opinion Mining)

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.


sentimentanalysis

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What You Need To Know about Text Analytics

You have enough to worry about. You know your industry inside and out. You know your products and services and how they compare with the competition’s strengths and weaknesses. In business, you have to be an expert in a range of topics. What you don’t need to worry about are the ins and outs of every technology, algorithm and software program.

This is especially true of an inherently complex technology such as natural language processing. As a business owner you have enough to worry about. Do you really have time to understand morphological segmentation? Text analytics should be just another tool in your toolbox to achieve your business goals. The only thing you need to know is what problems you have that can be solved by natural language processing. Anaphoric referencing? Don’t worry about it. We have it covered it, along with anything else you might need from language technology.

Text Analytics

What do you do need to know about text analytics?

Text analytics goes by many names: natural language processing (NLP), text analysis, text mining, computational linguistics. There are shades of difference in these terms, but let the expert work that out. What you need to know is that these terms describe a variety of algorithms and technology that is able to process raw text written in a human language (natural language) to provide enriched text. That enrichment could mean a number of things:

  • Categorization – Classifying text according to themes, categories or a taxonomy
  • Topic Extraction – Identifying key named entities and concepts in the text such as people, places, organizations, and brands
  • Sentiment Analysis – Detecting whether the text is talking about those concepts in a positive or negative light

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The Role of Text Mining in the Insurance Industry

What can insurance companies do to exploit all their unstructured information?

A typical big data scenario

Insurance companies collect huge volumes of text on a daily basis and through multiple channels (their agents, customer care centers, emails, social networks, web in general). The information collected includes policies, expert and health reports, claims and complaints, results of surveys, relevant interactions between customers and no-customers in social networks, etc. It is impossible to handle, classify, interpret or extract the essential information from all that material.

The Insurance Industry is among the ones that most can benefit from the application of technologies for the intelligent analysis of free text (known as Text Analytics, Text Mining or Natural Language Processing).

Insurance companies have to cope also with the challenge of combining the results of the analysis of these textual contents with structured data (stored in conventional databases) to improve decision-making. In this sense, industry analysts consider essential the use of multiple technologies based on Artificial Intelligence (intelligent systems), Machine Learning (data mining) and Natural Language Processing (both statistical and symbolic or semantic).

Most promising areas of text analytics in the Insurance Sector

Fraud detection

Detección de Fraude

According to Accenture, in a report released in 2013, it is estimated that in Europe insurance companies lose between 8,000 and 12,000 million euros per year due to fraudulent claims, with an increasing trend. Additionally, the industry estimates that between 5% and 10% of the compensations paid by the companies in the previous year were due to fraudulent reasons, which could not be detected due to the lack of predictive analytic tools.

According to the specialized publication “Health Data Management”, Medicare’s fraud prevention system in the United States, which is based on predictive algorithms that analyze patterns in the providers’ billing, in 2013 saved more than 200 million dollars in rejected payments.

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