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AG Corporate Semantic WebFreie Universität Berlin
http://www.inf.fu-berlin.de/groups/ag-csw/
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.dehttp://www.inf.fu-berlin.de/groups/ag-csw/
2AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Agenda
Data Opinion Mining Ontology Semantic Opinion Mining Conclusion
Data
4AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Data
Everything is data!
5AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Data
Structured Data
SQLData Warehousing
6AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Data
Unstructured Data
Sentiment Analysis or Opinion Mining
Opinion Mining
8AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Sentiment Analysis (SA)?
Related areas of sentiment analysis
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)
Sentiment Analysis
Data MiningData Mining Natural Language Processing
Natural Language Processing
Machine LearningMachine LearningInformation RetrievalInformation Retrieval
SAText Mining
9AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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
BUT
Semantic Web is a good Idea - Ontology
12AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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
13AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
OntologyOntology 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
andsemantically richer than common approaches for Databases.
The information that is described by an ontology consists
of semi-structured natural language texts and not tabularinformation.
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 thestructure of a data container.
14AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
OntologySize and scope of an ontology
Two extremes : One (small) ontology for each specific application One huge ontology that captures "everything"
OA A
A
Domain related ontologyGeneral purpose ontology
Semantic Opinion Mining
16AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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
17AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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.
18AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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 subattribute of image quality.
19AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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
Landscape Photos
ZoomFlash NoiseNoiseLensLens
20AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Example Structure
Ontology-based feature level opinion mining in Portuguese reviews is applied
Ontology (concepts, properties, instances and hierarchies) for feature identification
[ Larissa A. and Renata Vieira, 2013 ]
21AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Example Structure
Some concepts of Movie Ontology
22AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
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
23AG Corporate Semantic Webhttp://www.inf.fu-berlin.de/groups/ag-csw/
Thank You!
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