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A Semantic Metadata Enrichment Software Ecosystem based on Sentiment and Emotion Metadata Enrichments


Ronald Brisebois, Alain Abran, Apollinaire Nadembega, Philippe Ntechobo
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Information retrieval and analysis is frequently used to extract meaningful knowledge from the unstructured web and long texts. As existing computer search engines struggle to understand the meaning of natural language, semantically sentiment and emotion enriched metadata may improve search engine capabilities and user finding. A semantic metadata enrichment software ecosystem (SMESE) has been proposed in our previous research. This paper presents an enhanced version of this ecosystem with a sentiment and emotion metadata enrichments algorithm. This paper proposes a model and an algorithm enhancing search engines finding contents according to the user interests, through text analysis approaches for sentiment and emotion analysis. It presents the design, implementation and evaluation of an engine harvesting and enriching metadata related to sentiment and emotion analysis. It includes the SSEA (Semantic Sentiment and Emotion Analysis) semantic model and algorithm that discover and enrich sentiment and emotion metadata hidden within the text or linked to multimedia structure. The performance of sentiment and emotion analysis enrichments is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques. The results show that the algorithm SSEA enable greater understanding and finding of document or contents associated with sentiment and emotion enriched metadata.

Ronald Brisebois, Alain Abran, Apollinaire Nadembega, Philippe Ntechobo

Emotion Analysis, Natural Language Processing, Semantic Metadata Enrichment, Sentiment Analysis, Text And Data Mining

  1. O. Appel, F. Chiclana, J. Carter, and H. Fujita, “A hybrid approach to the sentiment analysis problem at the sentence level,” Knowledge-Based Systems, vol. 108, pp. 110-124, 2016. doi:http://dx.doi.org/10.1016/j.knosys.2016.05.040
  2. R. Brisebois, A. Abran, and A. Nadembega, “A Semantic Metadata Enrichment Software Ecosystem (SMESE) based on a Multi-platform Metadata Model for Digital Libraries,” Accepted for publication in Journal of Software Engineering and Applications (JSEA), vol. 10, no. 04, 2017
  3. G. Salton, and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing & Management, vol. 24, no. 5, pp. 513-523, 1988. doi:http://dx.doi.org/10.1016/0306-4573(88)90021-0
  4. T. Niu, S. Zhu, L. Pang, and A. El Saddik, “Sentiment Analysis on Multi-View Social Data,” in 22nd International Conference on MultiMedia Modeling (MMM), Miami, FL, USA, 2016, pp. 15-27. doi:http://dx.doi.org/10.1007/978-3-319-27674-8_2
  5. K. Bougiatiotis, and T. Giannakopoulos, “Content Representation and Similarity of Movies based on Topic Extraction from Subtitles,” in Proceedings of the 9th Hellenic Conference on Artificial Intelligence, Thessaloniki, Greece, 2016, pp. 1-7. doi:http://dx.doi.org/10.1145/2903220.2903235
  6. G. A. Patel, and N. Madia, “A Survey: Ontology Based Information Retrieval For Sentiment Analysis,” International Journal of Scientific Research in Science, Engineering and Technology, vol. 2, no. 2, pp. 460-465, 2016
  7. J. A. Balazs, and J. D. Velásquez, “Opinion Mining and Information Fusion: A survey,” Information Fusion, vol. 27, pp. 95-110, 2016. doi:http://dx.doi.org/10.1016/j.inffus.2015.06.002
  8. K. Ravi, and V. Ravi, “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications,” Knowledge-Based Systems, vol. 89, pp. 14-46, 2015. doi:http://dx.doi.org/10.1016/j.knosys.2015.06.015
  9. J. Serrano-Guerrero, J. A. Olivas, F. P. Romero, and E. Herrera-Viedma, “Sentiment analysis: A review and comparative analysis of web services,” Information Sciences, vol. 311, pp. 18-38, 2015. doi:http://dx.doi.org/10.1016/j.ins.2015.03.040
  10. M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede, “Lexicon-based methods for sentiment analysis,” Comput. Linguist., vol. 37, no. 2, pp. 267-307, 2011. doi:10.1162/COLI_a_00049
  11. D. Vilares, M. A. Alonso, and C. GÓMez-RodrÍGuez, “A syntactic approach for opinion mining on Spanish reviews,” Natural Language Engineering, vol. 21, no. 1, pp. 139-163, 2015. doi:http://dx.doi.org/10.1017/S1351324913000181
  12. S. Kiritchenko, X. Zhu, and S. M. Mohammad, “Sentiment analysis of short informal texts,” Journal of Artificial Intelligence Research, vol. 50, no. 1, pp. 723-762, 2014. doi:http://dx.doi.org/10.1613/jair.4272
  13. D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent Dirichlet Allocation,” Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003
  14. S. T. Dumais, “Latent semantic analysis,” Annual Review of Information Science and Technology, vol. 38, no. 1, pp. 188-230, 2004. doi:10.1002/aris.1440380105
  15. J. Cigarrán, Á. Castellanos, and A. García-Serrano, “A step forward for Topic Detection in Twitter: An FCA-based approach,” Expert Systems with Applications, vol. 57, pp. 21-36, 2016. doi:http://dx.doi.org/10.1016/j.eswa.2016.03.011
  16. P. Chen, N. L. Zhang, T. Liu, L. K. M. Poon, and Z. Chen, “Latent Tree Models for Hierarchical Topic Detection,” arXiv preprint arXiv:1605.06650 cs.CL], pp. 1-44, 2016
  17. R. Moraes, J. F. Valiati, and W. P. Gavião Neto, “Document-level sentiment classification: An empirical comparison between SVM and ANN,” Expert Systems with Applications, vol. 40, no. 2, pp. 621-633, 2013. doi:http://dx.doi.org/10.1016/j.eswa.2012.07.059
  18. M. Ghiassi, J. Skinner, and D. Zimbra, “Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network,” Expert Systems with Applications, vol. 40, no. 16, pp. 6266-6282, 2013. doi:http://dx.doi.org/10.1016/j.eswa.2013.05.057
  19. A. Gangemi, “A Comparison of Knowledge Extraction Tools for the Semantic Web,” in 10th European Semantic Web Conference (ESWC), Montpellier, France, 2013, pp. 351-366. doi:http://dx.doi.org/10.1007/978-3-642-38288-8_24
  20. S. N. Shivhare, and S. Khethawat, “Emotion Detection from Text,” in Second International Conference on Computer Science, Engineering and Applications (ICCSEA), Delhi, India, 2012, pp. 1-7
  21. A. Moreo, M. Romero, J. L. Castro, and J. M. Zurita, “Lexicon-based Comments-oriented News Sentiment Analyzer system,” Expert Systems with Applications, vol. 39, no. 10, pp. 9166-9180, 2012. doi:http://dx.doi.org/10.1016/j.eswa.2012.02.057
  22. C. Bosco, V. Patti, and A. Bolioli, “Developing corpora for sentiment analysis: The case of irony and senti-tut,” IEEE Intelligent Systems, vol. 28, no. 2, pp. 55-63, 2013
  23. H. Cho, S. Kim, J. Lee, and J.-S. Lee, “Data-driven integration of multiple sentiment dictionaries for lexicon-based sentiment classification of product reviews,” Knowledge-Based Systems, vol. 71, pp. 61-71, 2014. doi:http://dx.doi.org/10.1016/j.knosys.2014.06.001
  24. C. Lin, Y. He, R. Everson, and S. Ruger, “Weakly Supervised Joint Sentiment-Topic Detection from Text,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 6, pp. 1134-1145, 2012. doi:http://dx.doi.org/10.1109/TKDE.2011.48
  25. E. Kontopoulos, C. Berberidis, T. Dergiades, and N. Bassiliades, “Ontology-based sentiment analysis of twitter posts,” Expert Systems with Applications, vol. 40, no. 10, pp. 4065-4074, 2013. doi:http://dx.doi.org/10.1016/j.eswa.2013.01.001
  26. B. Desmet, and V. Hoste, “Emotion detection in suicide notes,” Expert Systems with Applications, vol. 40, no. 16, pp. 6351-6358, 2013. doi:http://dx.doi.org/10.1016/j.eswa.2013.05.050
  27. M. Abdul-Mageed, M. Diab, and S. Kübler, “SAMAR: Subjectivity and sentiment analysis for Arabic social media,” Computer Speech & Language, vol. 28, no. 1, pp. 20-37, 2014. doi:http://dx.doi.org/10.1016/j.csl.2013.03.001
  28. L. K.-W. Tan, J.-C. Na, Y.-L. Theng, and K. Chang, “Phrase-Level Sentiment Polarity Classification Using Rule-Based Typed Dependencies and Additional Complex Phrases Consideration,” Journal of Computer Science and Technology, vol. 27, no. 3, pp. 650-666, 2012. doi:http://dx.doi.org/10.1007/s11390-012-1251-y
  29. L. Chen, L. Qi, and F. Wang, “Comparison of feature-level learning methods for mining online consumer reviews,” Expert Systems with Applications, vol. 39, no. 10, pp. 9588-9601, 2012. doi:http://dx.doi.org/10.1016/j.eswa.2012.02.158
  30. C. Quan, and F. Ren, “Unsupervised product feature extraction for feature-oriented opinion determination,” Information Sciences, vol. 272, pp. 16-28, 2014. doi:http://dx.doi.org/10.1016/j.ins.2014.02.063
  31. S. Poria, E. Cambria, A. Hussain, and G.-B. Huang, “Towards an intelligent framework for multimodal affective data analysis,” Neural Networks, vol. 63, pp. 104-116, 2015. doi:http://dx.doi.org/10.1016/j.neunet.2014.10.005
  32. M. D. Munezero, C. S. Montero, E. Sutinen, and J. Pajunen, “Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text,” IEEE Transactions on Affective Computing, vol. 5, no. 2, pp. 101-111, 2014. doi:http://dx.doi.org/10.1109/TAFFC.2014.2317187
  33. W. Li, and H. Xu, “Text-based emotion classification using emotion cause extraction,” Expert Systems with Applications, vol. 41, no. 4, Part 2, pp. 1742-1749, 2014. doi:http://dx.doi.org/10.1016/j.eswa.2013.08.073
  34. S. Bao, S. Xu, L. Zhang, R. Yan, Z. Su, D. Han, and Y. Yu, “Mining Social Emotions from Affective Text,” IEEE Transactions on Knowledge and Data Engineering, vol. 24, no. 9, pp. 1658-1670, 2012. doi:http://dx.doi.org/10.1109/TKDE.2011.188
  35. S. V. Kedar, D. S. Bormane, A. Dhadwal, S. Alone, and R. Agarwal, “Automatic Emotion Recognition through Handwriting Analysis: A Review,” in 2015 International Conference on Computing Communication Control and Automation (ICCUBEA), Pune, India, 2015, pp. 811-816. doi:http://dx.doi.org/10.1109/ICCUBEA.2015.162
  36. J. Lei, Y. Rao, Q. Li, X. Quan, and L. Wenyin, “Towards building a social emotion detection system for online news,” Future Generation Computer Systems, vol. 37, pp. 438-448, 2014. doi:http://dx.doi.org/10.1016/j.future.2013.09.024
  37. V. Anusha, and B. Sandhya, "A Learning Based Emotion Classifier with Semantic Text Processing," Advances in Intelligent Informatics, M. E.-S. El-Alfy, M. S. Thampi, H. Takagi, S. Piramuthu and T. Hanne, eds., pp. 371-382, Cham, Switzerland: Springer International Publishing, 2015. doi:http://dx.doi.org/10.1007/978-3-319-11218-3_34
  38. E. Cambria, P. Gastaldo, F. Bisio, and R. Zunino, “An ELM-based model for affective analogical reasoning,” Neurocomputing, vol. 149, Part A, pp. 443-455, 2015. doi:http://dx.doi.org/10.1016/j.neucom.2014.01.064
  39. M. F. Porter, “An algorithm for suffix stripping,” Program, vol. 14, no. 3, pp. 130-137, 1980. doi:doi:10.1108/eb046814
  40. de Marneffe M-C, MacCartney B, and Manning CD, “Generating typed dependency parsers from phrase structure parses ” in fifth international conference on language resources and evaluation, GENOA , ITALY 2006, pp. 449–54
  41. Y. Tsuruoka, and J. i. Tsujii, “Bidirectional inference with the easiest-first strategy for tagging sequence data,” in Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, Vancouver, British Columbia, Canada, 2005, pp. 467-474. doi:10.3115/1220575.1220634
  42. C. Zhang, H. Wang, L. Cao, W. Wang, and F. Xu, “A hybrid term–term relations analysis approach for topic detection,” Knowledge-Based Systems, vol. 93, pp. 109-120, 2016. doi:http://dx.doi.org/10.1016/j.knosys.2015.11.006
  43. H. Sayyadi, and L. Raschid, “A Graph Analytical Approach for Topic Detection,” ACM Transactions on Internet Technology, vol. 13, no. 2, pp. 1-23, 2013. doi:http://dx.doi.org/10.1145/2542214.2542215

Publication Details

Published in : Volume 3 | Issue 2 | March-April - 2017
Date of Publication Print ISSN Online ISSN
2017-04-23 2395-1990 2394-4099
Page(s) Manuscript Number   Publisher
625-641 IJSRSET1732170   Technoscience Academy

Cite This Article

Ronald Brisebois, Alain Abran, Apollinaire Nadembega, Philippe Ntechobo, "A Semantic Metadata Enrichment Software Ecosystem based on Sentiment and Emotion Metadata Enrichments", International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 2, pp.625-641, March-April-2017.
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