ontology based sentiment analysis

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?