WordNet: a lexical database for English
Communications of the ACM
Knowledge engineering: principles and methods
Data & Knowledge Engineering - Special jubilee issue: DKE 25
Formal Concept Analysis: Mathematical Foundations
Formal Concept Analysis: Mathematical Foundations
ARSA: a sentiment-aware model for predicting sales performance using blogs
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Ontology learning from domain specific web documents
International Journal of Metadata, Semantics and Ontologies
Sentiment analysis of blogs by combining lexical knowledge with text classification
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Utility Ontology Development with Formal Concept Analysis
Proceedings of the 2008 conference on Formal Ontology in Information Systems: Proceedings of the Fifth International Conference (FOIS 2008)
Using emoticons to reduce dependency in machine learning techniques for sentiment classification
ACLstudent '05 Proceedings of the ACL Student Research Workshop
OntoGen: semi-automatic ontology editor
Proceedings of the 2007 conference on Human interface: Part II
Ontological reasoning to configure emotional voice synthesis
RR'07 Proceedings of the 1st international conference on Web reasoning and rule systems
CIMSIM '10 Proceedings of the 2010 Second International Conference on Computational Intelligence, Modelling and Simulation
Using Formal Concept Analysis for Maritime Ontology Building
IFITA '10 Proceedings of the 2010 International Forum on Information Technology and Applications - Volume 02
Robust sentiment detection on Twitter from biased and noisy data
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
Proceedings of the ACM 2011 conference on Computer supported cooperative work
Lexicon-based methods for sentiment analysis
Computational Linguistics
Sentiment analysis of Twitter data
LSM '11 Proceedings of the Workshop on Languages in Social Media
Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews
Expert Systems with Applications: An International Journal
Text2Onto: a framework for ontology learning and data-driven change discovery
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
Towards semantically-interlinked online communities
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
Lexicon-based Comments-oriented News Sentiment Analyzer system
Expert Systems with Applications: An International Journal
Potential Power and Problems in Sentiment Mining of Social Media
International Journal of Strategic Decision Sciences
Text-based emotion classification using emotion cause extraction
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.