Semantic opinion mining ontology

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<ul><li> 1. Feature Extractions based Semantic Sentiment Analysis Mohammed Al-Mashraee Supervisor: Prof. Dr. Adrian Paschke Corporate Semantic Web (AG-CSW) Institute for Computer Science, Freie Universitt Berlin almashraee@inf.fu-berlin.de http://www.inf.fu-berlin.de/groups/ag-csw/AG Corporate Semantic Web Freie Universitt Berlin http://www.inf.fu-berlin.de/groups/ag-csw/</li></ul> <p> 2. Agenda Data Opinion Mining Ontology Semantic Opinion Mining ConclusionAG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/2 3. Data 4. Data Everything is data!AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/4 5. Data Structured DataSQL Data Warehousing AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/5 6. Data Unstructured DataSentiment Analysis or Opinion Mining AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/6 7. Opinion Mining 8. Sentiment Analysis (SA)? Sentiment analysis, also called opinion mining, is the field of study that analyzes peoples 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 MiningSA Machine Learning Machine LearningInformation Retrieval Information Retrieval Sentiment AnalysisNatural Language Natural Language Processing ProcessingData Mining Data MiningRelated areas of sentiment analysis AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/8 9. 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 informationAG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/9 10. BUT 11. Semantic Web is a good Idea - Ontology 12. 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 machinesAG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/12 13. 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. AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/13 14. Ontology Size and scope of an ontology Two extremes : One (small) ontology for each specific application One huge ontology that captures "everything" AAOADomain related ontology AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/General purpose ontology 14 15. Semantic Opinion Mining 16. 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 themAG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/16 17. 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.AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/17 18. 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.AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/18 19. 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 PhotosFlashLens LensAG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/Landscape PhotosNoise NoiseZoom19 20. Example Structure [ Larissa A. and Renata Vieira, 2013 ]Ontology-based feature level opinion mining in Portuguese reviews is appliedOntology (concepts, properties, instances and hierarchies) for feature identification AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/20 21. Example StructureSome concepts of Movie Ontology AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/21 22. 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 mentionedAG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/22 23. Thank You!AG Corporate Semantic Web http://www.inf.fu-berlin.de/groups/ag-csw/23 </p>