By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: When Telecom customers complain-Pt. 2
Share
Notification Show More
Latest News
ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Uncategorized > When Telecom customers complain-Pt. 2
Uncategorized

When Telecom customers complain-Pt. 2

ThemosKalafatis
Last updated: 2008/12/14 at 2:53 PM
ThemosKalafatis
4 Min Read
SHARE
On the previous post I explained the first steps in deploying Information Extraction, Text Mining and Computational Linguistics to capture the essence of Telecom customers complaints.
We have already discussed about the big picture: Retrieve data (essentially user messages from forums) and then use Information Extraction to transform unstructured information to a structured form. This transformation is done by building a set of matching rules for…

On the previous post I explained the first steps in deploying Information Extraction, Text Mining and Computational Linguistics to capture the essence of Telecom customers complaints.
We have already discussed about the big picture: Retrieve data (essentially user messages from forums) and then use Information Extraction to transform unstructured information to a structured form. This transformation is done by building a set of matching rules for specific phrases or keywords, such as
-signal
-antenna
-customer care
and words of sentiment such as
-worse
-worst
-better
-best
-outraged
among many others.
So here comes the interesting part: Suppose a telecom company has in its possession an application that is able to search and extract sentiment from unstructured information. Having such a tool means that :
  • A user can query directly on user forums — for example, specific network problems — and break down those problems by area name.
  • A user can directly query for hot phrases such as “canceling my subscription” and cluster keywords around those messages. If the telecom company is also running (and most likely it is running) churn prediction models, then analysts have yet another source to cross-check and/or enhance the conclusions of their churning models with this new information.
  • Special matching rules can be applied to extract why users prefer company XYZ over company ABC.
  • This technology can be applied to e-mails and/or free text complaints to the customer care center, which means that analysts can further enhance their churning models with additional data.
  • Matching rules can be built that associate keywords to Telecom companies in terms of their co-occurrence. So telecom company XYZ has the phrase “good signal” associated with its brand whilst company ABC has the phrase “bargain” as the associated keyword.
  • Match billing plan keywords and then cluster them with sentiment keywords. In other words, how do customers perceived the new billing plan and what is the sentiment about it?
It is easy to realize that Information Extraction combined with Text Mining and linguistics is a powerful combination that can extract many “knowledge nuggets”. The fact that such an application cannot be 100% accurate may arise acceptance problems but its sure worth the effort in the end if potential problems are clearly presented before implementation of this application.
Let us not forget that a complaint given by a customer to the customer center remains there – between the boundaries of the company. A complaint posted on a forum can be seen by hundreds of thousands of others (and it will most likely stay there for a long time), influencing potential and existing customers in a non-positive way.
A Sentiment mining application may be also used for:
  • Banking
  • Pharmaceuticals
  • Insurance
  • Consumer Products (Customer Reviews)
…and of course for capturing the sentiment of citizens for politicians.

Link to original post

ThemosKalafatis December 14, 2008
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

big data improves
Big DataJobsKnowledge ManagementUncategorized

3 Ways Big Data Improves Leadership Within Companies

6 Min Read
Image
Uncategorized

IT Is Not Analytics. Here’s Why.

7 Min Read

Romney Invokes Analytics in Rebuke of Trump

4 Min Read

WEF Davos 2016: Top 100 CEO bloggers

14 Min Read

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

AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?