ontology based sentiment analysis
DESCRIPTION
I created this presentation to present my research work to the committee. My research was on extracting tweets and analyzing it with an previously created ontology model. The results of the ontology model will help in identifying the domain area of the problem for which use had shared negative sentiments on tweeter. This system along with the ontology model developed for Postal service domain. The next step in research will be to generate automated responses on twitter to the user who shares negative sentiments.TRANSCRIPT
ONTOLOGY-BASED SENTIMENT ANALYSIS MODEL
By
Pratik ThakorDepartment of Computer and Information Science
Advisor: Dr. Sreela Sasi May 2, 2014
OVERVIEW Introduction Problem Background Research Proposed Solution Architecture Results Conclusion
INTRODUCTION Social media connects organizations and customers. i.e.
Twitter, Facebook and Google+.
Use of social media: Organization
Get product feedbacks Promote brand value Directly connect with customers.
Customer Get product updates Build and connect with product user community Share experience
PROBLEM Organizations:
Read direct feedbacks Generate report of satisfaction/dissatisfactions Communicate
NO Interactive communication for user’s complaint on
social media
A system is needed Can extract social media content & analyze Identify the reason for problem Generate the response on the social media platform
BACKGROUND RESEARCH Sayed Zeesan Haider, “Ontology-based sentiment analysis
case study”, a case study for Master degree project, University of Skovde, pages 05-67, 2012.
Built cell phone feature-based ontology model Analyzed the customer review
K.M Sam and C.R. Chatwin, “Ontology-Based Sentiment Analysis Model of Customer reviews for Electronic Products”, Proceedings of International Journal of e-Business, e-Management and e-Learning.
Built the customer satisfaction model
BACKGROUND RESEARCH Tim Finin, Li Ding and Lina Zou “Social Networking
on the Semantic Web”, Learning Organization Journal, special issue on Ubiquitous Business Intelligence, Miltiadis Lytras et al, 2005.
Ontology-based intelligent application
Natalya F. Noy and Deborah L. McGuinness “A guide to creating your first ontology”, Stanford University.
Ontology building
PROPOSED SOLUTION An ontology-based sentiment analysis model and an
automated response generator system.
Architecture of the model - three processes Ontology model creation process Sentiment analysis with ontology model (Identifying
the associated problem with the content) Automated response generator
ARCHITECTURE PROCESS 1
ARCHITECTURE PROCESS 2
ARCHITECTURE PROCESS 3
ARCHITECTURE - MODULES Data extraction: Extract data from Twitter GATE software: Extract information like nouns and
verbs from the content Protégé software: Build ontology model and to query
the model Ontology model: Consists class, subclass, objects,
object properties SentiStrength2: Identify positive and negative
sentiments tweets. SPARQL query language: Query the ontology model
and retrieve the information
SAMPLE TWEETS
GATE SOFTWARE OUTPUT
GATE SOFTWARE FILE OUTPUT
SAMPLE ONTOLOGY MODEL
QUERY BUILDING
INFORMATION RETRIEVAL FROM ONTOLOGY MODEL
SENTISTRENGTH2 RESULTS
CONCLUSION “We can develop a system to analyze negative
content being shared on social media platform and try to find out problem associated with it. After understanding the problem, it is possible to generate predefine reply for it on social media.”
This model will help in building foundation for further research on the use of ontology for sentimental analysis.
REFERENCE Sayed Zeesan Haider, Ontology-based sentiment
analysis case study, University of Skovde, pages 05-67, 2012
K.M. Sam and C.R. Chatwin, Ontology-based Sentiment Analysis Model of Customer reviews for Electronic Products, Proceedings of International Journal of e-Business, e-Management and e-Learning, Vol. 3, No. 6, December 2013
Larissa A. de Freitas and Renata Vieira, Ontology-based Feature Level Opinion Mining for Portuguese Reviews, PUCRS FACIN, Porto Alegre, Brazil, 2013
REFERENCE Bing Liu, “Sentiment Analysis and Subjectivity”,
from Handbook of Natural Language Processing, Second Edition, (editors: N. Indurkhya and F. J. Damerau), 2010
Matteo Baldoni, Cristina Baroglio, Viviana Patti and Paolo Rena, “From Tags to Emotions: Ontology-driven Sentiment Analysis in the Social Semantic Web”, Universit`a degli Studi di Torino, 2010
Natalya F. Noy and Deborah L. McGuinness “A guide to creating your first ontology”, Stanford University
QUESTION?