Semantic opinion mining ontology

Feature Extractions based Semantic Sentiment Analysis
Mohammed Al-Mashraee
Supervisor: Prof. Dr. Adrian Paschke
Corporate Semantic Web (AG-CSW)
Institute for Computer Science,
Freie Universität Berlin
almashraee@inf.fu-berlin.de
http://www.inf.fu-berlin.de/groups/ag-csw/

AG Corporate Semantic Web
Freie Universität Berlin

Agenda
 Data
 Opinion Mining
 Ontology
 Semantic Opinion Mining
 Conclusion


2

Data
Everything is data!


4

Data
Structured Data

SQL
Data Warehousing

5

Data
Unstructured Data

Sentiment Analysis or Opinion Mining

6

Sentiment Analysis (SA)?
Sentiment analysis, also called opinion mining, is the field of study
that analyzes people’s opinions, sentiments, evaluations, appraisals,
attitudes, and emotions towards entities such as products, services,
organizations, individuals, issues, events, topics, and their attributes.
(Bing Liu 2012)

Text Mining

SA
Machine Learning
Machine Learning

Information Retrieval
Information Retrieval
Sentiment Analysis

Natural Language
Natural Language
Processing
Processing

Data Mining
Data Mining

Related areas of sentiment analysis

8

SA …. Why?
 Unstructured Text
− Huge amount of information is shared by the
organizations across the world over the web
− Huge amount of text scattered over many user
generated contents resources
−

methods, systems and related tools that are
successfully converting structured information
into business intelligence, simply are ineffective
when applied for unstructured information


9

Semantic Web is a
good Idea - Ontology

Ontology


An ontology is an explicit specification of a conceptualization
[Gruber93]
 An ontology is a shared understanding of some domain of
interest. [Uschold, Gruninger96]
 There are many definitions
− a formal specification EXECUTABLE
− of a conceptualization of a domain COMMUNITY
− of some part of world that is of interest APPLICATION
 Defines
− A common vocabulary of terms
− Some specification of the meaning of the terms
− A shared understanding for people and machines


12

Ontology
Ontology and DB schema


An ontology provides an explicit conceptualisation that describe
the semantics of the data. They have a similar function as a
database schema. The differences are:
− A language for defining Ontologies is syntactically and
semantically richer than common approaches for Databases.
− The information that is described by an ontology consists
of semi-structured natural language texts and not tabular
information.
− An ontology must be a shared and consensual terminology
because it is used for information sharing and exchange.
− An ontology provides a domain theory and not the
structure of a data container.

13

Ontology
Size and scope of an ontology
Two extremes :
 One (small) ontology for each specific application
 One huge ontology that captures "everything"
A

A

O

A

Domain related ontology

General purpose ontology
14

Semantic Opinion Mining
 Semantics to opinion mining is realyzed by
building a datailed ontology for a particular
domain
 Ontologies can be used to structure information
 Ontologies provide a formal, structured
knowledge representation with the advantage
of being reusable and sharable
 Ontologies provide a common vocabulary for a
domain and define the meaning of the
attributes and the relations between them


16

Semantic Opinion Mining
 Useful feature could be identified using
ontologies
 Once the required features have been
identified, opinion mining approaches are used
to get an efficient sentiment classification.


17

Examples
Example1:
 In the domain of digital camera: comments, reviews,
or sentences on image quality are usually mentioned.
 However, a sentence like the following:
„40D handles noise very well up to ISO 800 “
Noise in the above example is a sub feature or sub
attribute of image quality.


18

Examples
Example2:
 Product features mentioned in reviews might be sub
attributes of more than one of other attributes in higher
levels in different degree of connections
Night
Photos

Flash

Lens
Lens


Landscape
Photos

Noise
Noise

Zoom

19

Example Structure
[ Larissa A. and Renata Vieira, 2013 ]

Ontology-based feature level opinion mining in Portuguese
reviews is applied

Ontology (concepts, properties, instances and hierarchies) for feature identification

20

Example Structure

Some concepts of Movie Ontology

21

Conclusion
 Opinion mining and sentiment analysis idea is
introduced
 Ontologies based Semantic Web is defined
 The usefulness of building ontologies to improve
the results of opinion mining is further
mentioned


22

Thank You!


23

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