Cookies help us display personalized product recommendations and ensure you have great shopping experience.

By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    image fx (67)
    Improving LinkedIn Ad Strategies with Data Analytics
    9 Min Read
    big data and remote work
    Data Helps Speech-Language Pathologists Deliver Better Results
    6 Min Read
    data driven insights
    How Data-Driven Insights Are Addressing Gaps in Patient Communication and Equity
    8 Min Read
    pexels pavel danilyuk 8112119
    Data Analytics Is Revolutionizing Medical Credentialing
    8 Min Read
    data and seo
    Maximize SEO Success with Powerful Data Analytics Insights
    8 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: When Telecom customers complain
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > When Telecom customers complain
Uncategorized

When Telecom customers complain

ThemosKalafatis
ThemosKalafatis
4 Min Read
SHARE
Probably one of the best uses of Information Extraction, Text mining and Computational Linguistics combined together, is their ability to show us the sentiment of customers. Today we are going to see an example for capturing the sentiment of Telecom customers.
When a customer writes his/her opinion on a forum, a wealth of information is generated because -more importantly- a customer uses words and phrases that cannot be found during a controll…

Probably one of the best uses of Information Extraction, Text mining and Computational Linguistics combined together, is their ability to show us the sentiment of customers. Today we are going to see an example for capturing the sentiment of Telecom customers.
When a customer writes his/her opinion on a forum, a wealth of information is generated because -more importantly- a customer uses words and phrases that cannot be found during a controlled study. The words, phrases and expressions are far more emotionally powerful than a Likert scale answer of type “Totally disagree / agree”.

So let us see the steps required :

First Step : The first thing of course is to actually find the data : User forums where people talk about mobile phones and mobile companies is obviously the place to look and there are lots of those places. Perhaps the volume of the messages is not enough but usually the available information is more than enough. Special code can be written to extract text from posts but without loss of the nature of the posting. As an example, the fact that a post has generated 20 replies is considered valuable information. The more posted replies, the more sentiment exists and this information has to be taken into consideration.

Second Step : Deploy information extraction techniques to identify phrases of good or bad sentiment (and actually many other things) about Telecom keywords such as :

– Signal
– Customer Care
– Billing

….etc

The following screen capture shows an example which is in Greek but i will provide all necessary explanation – Please also note that this is a simplified version of the process :

Notice that on the right hand-side there are some bars that denote the type of keywords found : The first category is called “Characterization” and if it is checked (which on the above screen capture it is) the software will highlight posts that only have some kind of characterization, whether good or bad. Notice also the yellow bar which has the name “Network”. Because it is checked, words that are synonyms of “Network” are highlighted and indeed this is the case because

Signal = σήμα (in Greek) and
Flawless = άψογο

so the highlighted phrase άψογο σήμα means “flawless signal”, which is a good characterization for the signal of two particular telecom companies. Notice also a line under the “Features” tab which says that between positions 3425 to 3429 there is a mention about signal (“mentionsSignal = true”).

Again, i have to point out that this is a simplified version of the process. Text Mining and Information Extraction is actually very hard work but it is also very rewarding for those that ultimately deploy and use it. On the next post we will see the problems (and there are many of them) but also how this unstructured information is turned to “nuggets of gold”.

Link to original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

power supplies for ATX for data scientists
Why Data Scientists Should Care About SFX Power Supplies
Big Data Exclusive
AI for website optimization
Free Tools to Test Website Accessibility
Artificial Intelligence Exclusive
Generative AI models
Thinking Machines At Work: How Generative AI Models Are Redefining Business Intelligence
Artificial Intelligence Business Intelligence Exclusive Infographic Machine Learning
image fx (2)
Monitoring Data Without Turning into Big Brother
Big Data Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

“We’re rapidly entering a world where everything can be monitored and measured,” said Erik…”

1 Min Read

SIA: Mobile Marketing

2 Min Read

Big Data, All Data, PureData, BLU Data

7 Min Read
Image
Uncategorized

First Look: IBM Operational Decision Manager Advanced

6 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data
ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-25 SmartData Collective. All Rights Reserved.
Go to mobile version
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?