Semantic opinion mining ontology

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/

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Agenda

Data Opinion Mining Ontology Semantic Opinion Mining Conclusion

Data

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Data

Everything is data!

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Data

Structured Data

SQLData Warehousing

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Data

Unstructured Data

Sentiment Analysis or Opinion Mining

Opinion Mining

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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

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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

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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

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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.

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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

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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

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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.

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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.

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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

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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 ]

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Example Structure

Some concepts of Movie Ontology

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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

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Thank You!