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
    sales and data analytics
    How Data Analytics Improves Lead Management and Sales Results
    9 Min Read
    data analytics and truck accident claims
    How Data Analytics Reduces Truck Accidents and Speeds Up Claims
    7 Min Read
    predictive analytics for interior designers
    Interior Designers Boost Profits with Predictive Analytics
    8 Min Read
    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
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: Making more sense out of Twitter Tweets
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > Making more sense out of Twitter Tweets
Data Mining

Making more sense out of Twitter Tweets

ThemosKalafatis
ThemosKalafatis
5 Min Read
SHARE
Over the last 5 posts I have described how unstructured text information from Twitter can be used for Knowledge Extraction. Specific examples were given such as Sentiment Mining for products (Amazon’s Kindle), Segmentation of Twitter users, and finally cluster analysis of the emotions and thoughts expressed from twitter users.
So far I have discussed some ways that text mining could help us in getting more insight on how people think. Now it is time to put Information Extraction and Ontologies to the equation.
Information Extraction (IE) is the automated extraction of any information such as (to name a few) Names (first names, city names, country names etc), facts or events from unstructured text. An example of IE was given in these posts where thousands of adverts of flats are extracted and then data mining analysis is performed to identify what characteristics are important for achieving a high renting price.
Ontologies are used for knowledge representation and may also be used for structuring the information that exists on the web…  

Over the last 5 posts I have described how unstructured text information from Twitter can be used for Knowledge Extraction. Specific examples were given such as Sentiment Mining for products (Amazon’s Kindle), Segmentation of Twitter users, and finally cluster analysis of the emotions and thoughts expressed from twitter users.
So far I have discussed some ways that text mining could help us in getting more insight on how people think. Now it is time to put Information Extraction and Ontologies to the equation.
Information Extraction (IE) is the automated extraction of any information such as (to name a few) Names (first names, city names, country names etc), facts or events from unstructured text. An example of IE was given in these posts where thousands of adverts of flats are extracted and then data mining analysis is performed to identify what characteristics are important for achieving a high renting price.
Ontologies are used for knowledge representation and may also be used for structuring the information that exists on the web. To give an example, consider the following product keywords :
  • Coke
  • Sprite
  • Dr Pepper
If one asks you what is common about them, your brain looks for generalizations and comes up with the following answers :
  • They are all Carbonated Drinks

  • (Possibly) they all contain sugar since the word “Diet” or “Zero” or “Light” is not mentioned.
Now let’s assume having an Ontology Engine that is able to do this and to be able to infer automatically that all these products are sugar-carbonated drinks. Such an action enables us to extract facts in a more coherent way. The reason behind this is that we lessen the effect discussed on The Statistics of Everyday Talk and thus are able to capture growing trends such as people expressing their thoughts regarding carbonated drinks rather than matching “Coke”, “Sprite” and “Dr Pepper” individually. Without Ontologies such a trend could be easily missed.
By using Ontologies or taxonomies where applicable, an associations discovery algorithm can search in different levels of information detail. For example data miners usually employ taxonomic information (ex. Sprite, Coke, Pepsi = carbonated drinks) when performing associations discovery analysis on Super Markets and the effort of applying taxonomies almost always pays back in terms of the knowledge extracted regarding consumer behavior.
I have used Ontologies over the past three years and have seen them in action. The fact that with Ontologies one could possibly have access to inference and deductive reasoning techniques is of great use. The application of Information Extraction, Natural Language Processing and subsequent insertion of this information in an Ontological setting has many potential applications.

Link to original post

TAGGED:information extractionontologiestwitter
Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

sales and data analytics
How Data Analytics Improves Lead Management and Sales Results
Analytics Big Data Exclusive
ai in marketing
How AI and Smart Platforms Improve Email Marketing
Artificial Intelligence Exclusive Marketing
AI Document Verification for Legal Firms: Importance & Top Tools
AI Document Verification for Legal Firms: Importance & Top Tools
Artificial Intelligence Exclusive
AI supply chain
AI Tools Are Strengthening Global Supply Chains
Artificial Intelligence Exclusive

Stay Connected

1.2kFollowersLike
33.7kFollowersFollow
222FollowersPin

You Might also Like

Social Media Roundup for January 13

6 Min Read

The Social Web as a Customer Support Channel

4 Min Read

Are you a CTO? Do you use Twitter? See CTOlist.com

6 Min Read

Twitter Analytics : These words may be affecting your popularity

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.

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
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?