This document presents an ontology-based sentiment analysis model and automated response generation system. It discusses using an ontology to model product features and customer sentiments from social media data. The proposed system has three main processes: ontology creation, sentiment analysis using the ontology to identify problems, and generating automated responses. The document provides examples of building an ontology model from Twitter data and using it to retrieve sentiment information.

1. ONTOLOGY-BASED SENTIMENT
ANALYSIS MODEL
By
Pratik Thakor
Department of Computer and Information Science
Advisor: Dr. Sreela Sasi May 2, 2014

2. OVERVIEW
 Introduction
 Problem
 Background Research
 Proposed Solution
 Architecture
 Results
 Conclusion

3. 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

4. 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

5. 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

6. 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

7. 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

8. ARCHITECTURE PROCESS 1

9. ARCHITECTURE PROCESS 2

10. ARCHITECTURE PROCESS 3

11. 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

12. SAMPLE TWEETS

13. GATE SOFTWARE OUTPUT

14. GATE SOFTWARE FILE OUTPUT

15. SAMPLE ONTOLOGY MODEL

16. QUERY BUILDING

17. INFORMATION RETRIEVAL FROM
ONTOLOGY MODEL

18. SENTISTRENGTH2 RESULTS

19. 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.

20. 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

21. 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