ABSTRACT
The Word-Graph Sentiment Analysis Method is proposed to identify the sentiment that expressed in a microblog document using the sequence of the words that contains. The sequence of the words can be represented using graphs in which graph similarity metrics and classification algorithms can be applied to produce sentiment predictions. Experiments that were carried out with this method in a Twitter dataset validate the proposed model and allow us to further understand the metrics and the criteria that can be applied in words-graphs to predict the sentiment disposition of short, microblog documents.
- Agarwal, B. et al. 2015. Sentiment analysis using commonsense and context information. Computational intelligence and neuroscience. 2015, (2015), 30. Google ScholarDigital Library
- Aisopos, F. et al. 2012. Content vs. Context for Sentiment Analysis: A Comparative Analysis over Microblogs. Proceedings of the 23rd ACM Conference on Hypertext and Social Media (New York, NY, USA, 2012), 187--196. Google ScholarDigital Library
- Aisopos, F. et al. 2011. Sentiment analysis of social media content using N-Gram graphs. Proceedings of the 3rd ACM SIGMM international workshop on Social media (2011), 9--14. Google ScholarDigital Library
- Aisopos, F. et al. 2012. Textual and contextual patterns for sentiment analysis over microblogs. Proceedings of the 21st international conference companion on World Wide Web (2012), 453--454. Google ScholarDigital Library
- Baccianella, S. et al. 2010. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. (May 2010).Google Scholar
- Chagheri, S. et al. 2012. Feature vector construction combining structure and content for document classification. 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT) (Mar. 2012), 946--950.Google ScholarCross Ref
- Chen, Y. et al. 2009. Similarity-based Classification: Concepts and Algorithms. J. Mach. Learn. Res. 10, (Jun. 2009), 747--776. Google ScholarDigital Library
- Conte, D. et al. Challenging Complexity of Maximum Common Subgraph Detection Algorithms: A Performance Analysis of Three Algorithms on a Wide Database of Graphs.Google Scholar
- Giannakopoulos, G. et al. 2008. Summarization System Evaluation Revisited: N-gram Graphs. ACM Trans. Speech Lang. Process. 5, 3 (Oct. 2008), 5:1--5:39. Google ScholarDigital Library
- Gunn, S.R. 1998. Support Vector Machines for Classification and Regression.Google Scholar
- Huang, S. et al. 2013. Sentiment and Topic Analysis on Social Media: A Multi-task Multi-label Classification Approach. Proceedings of the 5th Annual ACM Web Science Conference (New York, NY, USA, 2013), 172--181. Google ScholarDigital Library
- IEEE Xplore Abstract - An ontology based sentiment analysis for mobile products using tweets: http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6921974&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6921974. Accessed: 2016-04-13.Google Scholar
- John, G. and Langley, P. 1995. Estimating Continuous Distributions in Bayesian Classifiers. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (1995), 338--345. Google ScholarDigital Library
- Khan, A.Z. et al. 2015. Combining lexicon-based and learning-based methods for Twitter sentiment analysis. International Journal of Electronics, Communication and Soft Computing Science & Engineering (IJECSCSE). (2015), 89.Google Scholar
- Kohavi, R. 1995. A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence - Volume 2 (San Francisco, CA, USA, 1995), 1137--1143. Google ScholarDigital Library
- Kontopoulos, E. et al. 2013. Ontology-based sentiment analysis of twitter posts. Expert Systems with Applications. 40, 10 (Aug. 2013), 4065--4074. Google ScholarDigital Library
- Liu, S.M. and Chen, J.-H. 2015. A Multi-label Classification Based Approach for Sentiment Classification. Expert Syst. Appl. 42, 3 (Feb. 2015), 1083--1093. Google ScholarDigital Library
- Maas, A.L. et al. 2011. Learning Word Vectors for Sentiment Analysis. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1 (Stroudsburg, PA, USA, 2011), 142--150. Google ScholarDigital Library
- Maas, A.L. et al. Multi-Dimensional Sentiment Analysis with Learned Representations.Google Scholar
- Maglogiannis, I.G. 2007. Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in EHealth, HCI, Information Retrieval and Pervasive Technologies. IOS Press.Google Scholar
- Mikolov, T. et al. 2013. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems (2013), 3111--3119.Google Scholar
- Narr, S. et al. 2012. Language-independent twitter sentiment analysis. Knowledge Discovery and Machine Learning (KDML), LWA. (2012), 12--14.Google Scholar
- Neviarouskaya, A. et al. 2010. Recognition of Affect, Judgment, and Appreciation in Text. Proceedings of the 23rd International Conference on Computational Linguistics (Stroudsburg, PA, USA, 2010), 806--814. Google ScholarDigital Library
- Nikolić, M. 2012. Measuring similarity of graph nodes by neighbor matching. Intelligent Data Analysis. 16, 6 (Jan. 2012), 865--878. Google ScholarDigital Library
- Pak, A. and Paroubek, P. 2010. Twitter as a Corpus for Sentiment Analysis and Opinion Mining. Proceedings of the International Conference on Language Resources and Evaluation, LREC 2010, 17-23 May 2010, Valletta, Malta (2010).Google Scholar
- Pal, M. 2008. Multiclass Approaches for Support Vector Machine Based Land Cover Classification. arXiv:0802.2411 {cs}. (Feb. 2008).Google Scholar
- Polymerou, E. et al. 2014. EmoTube: A Sentiment Analysis Integrated Environment for Social Web Content. Proceedings of the 4th International Conference on Web Intelligence, Mining and Semantics (WIMS14) (New York, NY, USA, 2014), 20:1--20:6. Google ScholarDigital Library
- Psomakelis, E. et al. 2014. Comparing methods for Twitter Sentiment Analysis. arXiv:1505.02973 {cs} (2014).Google Scholar
- Raymond, J.W. and Willett, P. 2002. Maximum common subgraph isomorphism algorithms for the matching of chemical structures. Journal of Computer-Aided Molecular Design. 16, 7 (Jul. 2002), 521--533.Google Scholar
- Sam, K. M and Chatwin, C. R. Ontology-Based Sentiment Analysis Model of Customer Reviews for Electronic Products. International Journal of e-Education, e-Business, e-Management and e-Learning.Google Scholar
- Taboada, M. et al. 2011. Lexicon-Based Methods for Sentiment Analysis. Computational Linguistics. 37, 2 (Apr. 2011), 267--307. Google ScholarDigital Library
- Vakali, A. and Kafetsios, K. Emotion aware clustering analysis as a tool for Web 2.0 communities detection: Implications for curriculum development.Google Scholar
- Violos, J. et al. 2014. Clustering Documents Using the 3-Gram Graph Representation Model. Proceedings of the 18th Panhellenic Conference on Informatics (New York, NY, USA, 2014), 29:1--29:5. Google ScholarCross Ref
- Wang, X. et al. 2007. Topical n-grams: Phrase and topic discovery, with an application to information retrieval. Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on (2007), 697--702. Google ScholarDigital Library
- Wilson, T. et al. 2005. Recognizing Contextual Polarity in Phrase-level Sentiment Analysis. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (Stroudsburg, PA, USA, 2005), 347--354. Google ScholarDigital Library
- Xu, Y. et al. 2007. A study on mutual information-based feature selection for text categorization. Journal of Computational Information Systems. 3, 3 (2007), 1007--1012.Google Scholar
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