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Sentiment Detection, Recognition and Aspect Identification

by Salma Faiz Alasmari, Mohammed Dahab
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 177 - Number 2
Year of Publication: 2017
Authors: Salma Faiz Alasmari, Mohammed Dahab
10.5120/ijca2017915675

Salma Faiz Alasmari, Mohammed Dahab . Sentiment Detection, Recognition and Aspect Identification. International Journal of Computer Applications. 177, 2 ( Nov 2017), 31-38. DOI=10.5120/ijca2017915675

@article{ 10.5120/ijca2017915675,
author = { Salma Faiz Alasmari, Mohammed Dahab },
title = { Sentiment Detection, Recognition and Aspect Identification },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2017 },
volume = { 177 },
number = { 2 },
month = { Nov },
year = { 2017 },
issn = { 0975-8887 },
pages = { 31-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume177/number2/28601-2017915675/ },
doi = { 10.5120/ijca2017915675 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:44:48.294876+05:30
%A Salma Faiz Alasmari
%A Mohammed Dahab
%T Sentiment Detection, Recognition and Aspect Identification
%J International Journal of Computer Applications
%@ 0975-8887
%V 177
%N 2
%P 31-38
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Sentiment analysis is the study of people’s opinions, attitudes, feelings, and emotions discuss any object such as entities, events, topics, product, issues, services, etc. are respected for extraction of useful subjective information out of the text. The task is challenging and considered for customers and producers, for instance, Customers need to have general ideas about products, and companies need to know customers’ needs and the opportunity to investigate and analyze those opinions towards their products and services. The increasing growth of the social network sites generating a big resource of social data. So, researchers considering about harnessing the effeteness of social media and sentiment analysis. These recent studies classified according to their contributions in the different SA methods. The main target of this paper is to give the illustration of the current trend of research in the sentiment analysis Detection, Recognition, Aspect Identification.

References
  1. T. Hartmann, S. Klenk, A. Burkovski, and G. Heidemann, "Sentiment Detection with Character n-Grams," in Proceedings of the 7th International Conference on Data Mining (DMIN’11), 2011, pp. 364-368.
  2. M. Devika, C. Sunitha, and A. Ganesh, "Sentiment Analysis: A Comparative Study on Different Approaches," Procedia Computer Science, vol. 87, pp. 44-49, 2016.
  3. H. Tang, S. Tan, and X. Cheng, "A survey on sentiment detection of reviews," Expert Systems with Applications, vol. 36, pp. 10760-10773, 2009.
  4. F. A. Pozzi, E. Fersini, E. Messina, and B. Liu, Sentiment Analysis in Social Networks: Morgan Kaufmann, 2016.
  5. . B. Liu, Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer, 2006.
  6. Liu, B. Sentiment analysis and subjectivity. In Handbook of Natural Language Processing, Second Edition, N. Indurkhya and F.J. Damerau, Editors. 2010.
  7. B. Liu and L. Zhang, "A survey of opinion mining and sentiment analysis," in Mining text data, ed: Springer, 2012, pp. 415-463.
  8. M. Dahab and S. Al Asmari, "Towards Discovering the Sentiment Holder in Arabic Text," International Journal of Computer Applications, vol. 111, 2015.
  9. Tan, S., Y. Wang., and X. Cheng. Combining learn-based and lexicon-based techniques for sentiment detection without using labeled examples. In Proceedings of ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR-2008), 2008.
  10. M. Giatsoglou, M. G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. C. Chatzisavvas, "Sentiment analysis leveraging emotions and word embeddings," Expert Systems with Applications, vol. 69, pp. 214-224, 2017.
  11. I. Hernández-Farías, J.-M. Benedí, and P. Rosso, "Applying basic features from sentiment analysis for automatic irony detection," in Iberian Conference on Pattern Recognition and Image Analysis, 2015, pp. 337-344.
  12. D. Maynard and A. Funk, "Automatic detection of political opinions in tweets," in Extended Semantic Web Conference, 2011, pp. 88-99.
  13. C. Lin, Y. He, and R. Everson, "A comparative study of Bayesian models for unsupervised sentiment detection," in Proceedings of the fourteenth conference on computational natural language learning, 2010, pp. 144-152.
  14. A. Severyn and A. Moschitti, "Twitter sentiment analysis with deep convolutional neural networks," in Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015, pp. 959-962.
  15. P. D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," in Proceedings of the 40th annual meeting on association for computational linguistics, 2002, pp. 417-424.
  16. S. M. Mohammad and P. D. Turney, "Crowdsourcing a word–emotion association lexicon," Computational Intelligence, vol. 29, pp. 436-465, 2013.
  17. J. Carrillo‐de‐Albornoz and L. Plaza, "An emotion‐based model of negation, intensifiers, and modality for polarity and intensity classification," Journal of the American Society for Information Science and Technology, vol. 64, pp. 1618-1633, 2013.
  18. B. Allison, "Sentiment detection using lexically-based classifiers," in International Conference on Text, Speech and Dialogue, 2008, pp. 21-28.
  19. M. Thelwall, "The Heart and Soul of the Web? Sentiment Strength Detection in the Social Web with SentiStrength," in Cyberemotions, ed: Springer, 2017, pp. 119-134.
  20. G. Qiu, X. He, F. Zhang, Y. Shi, J. Bu, and C. Chen, "DASA: dissatisfaction-oriented advertising based on sentiment analysis," Expert Systems with Applications, vol. 37, pp. 6182-6191, 2010.
  21. V. Patel, G. Prabhu, and K. Bhowmick, "A Survey of Opinion Mining and Sentiment Analysis," International Journal of Computer Applications, vol. 131, pp. 24-27, 2015.
  22. N. Jindal and B. Liu, "Opinion spam and analysis," in Proceedings of the 2008 International Conference on Web Search and Data Mining, 2008, pp. 219-230.
  23. Y.-R. Chen and H.-H. Chen, "Opinion spam detection in web forum: a real case study," in Proceedings of the 24th International Conference on World Wide Web, 2015, pp. 173-183.
  24. B. Wang, M. Liakata, A. Zubiaga, and R. Procter, "TDParse-multi-target-specific sentiment recognition on Twitter," 2016.
  25. D. T. Santosh And B. V. Vardhan, "Automatic Machine Recognition Of Features And Sentiments From Online Reviews," In Iaeng Transactions On Engineering Sciences: Special Issue For The International Association Of Engineers Conferences 2015, 2017, pp. 264-280.
  26. S. K. Bharti, K. S. Babu, and S. K. Jena, "Parsing-based sarcasm sentiment recognition in Twitter data," in Advances in Social Networks Analysis and Mining (ASONAM), 2015 IEEE/ACM International Conference on, 2015, pp. 1373-1380.
  27. Z. Liu, S. Liu, L. Liu, J. Sun, X. Peng, and T. Wang, "Sentiment recognition of online course reviews using multi-swarm optimization-based selected features," Neurocomputing, vol. 185, pp. 11-20, 2016.
  28. V. Jagtap and K. Pawar, "Analysis of different approaches to sentence-level sentiment classification," International Journal of Scientific Engineering and Technology, vol. 2, pp. 164-170, 2013.
  29. B. Liu, "Sentiment analysis and opinion mining," Synthesis lectures on human language technologies, vol. 5, pp. 1-167, 2012.
  30. C. R. Fink, D. S. Chou, J. J. Kopecky, and A. J. Llorens, "Coarse- and Fine-Grained Sentiment Analysis of Social Media Text," Johns Hopkins APL Technical Digest, vol. 30, pp. 22-30, 2011.
  31. J. Liang, X. Zhou, Y. Hu, L. Guo, and S. Bai, "Conr: A novel method for sentiment word identification," in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, 2014, pp. 1943-1946.
  32. O. Chunping, L. Yongbin, Z. Shuqing, and Y. Xiaohua, "Opinion Objects Identification and Sentiment Analysis," International Journal of Database Theory and Application, vol. 8, pp. 1-12, 2015.
  33. R. T. Khasawneh, H. A. Wahsheh, I. M. Alsmadi, and M. N. AI-Kabi, "Arabic sentiment polarity identification using a hybrid approach," in Information and Communication Systems (ICICS), 2015 6th International Conference on, 2015, pp. 148-153.
  34. S. Poria, I. Chaturvedi, E. Cambria, and A. Hussain, "Convolutional MKL based multimodal emotion recognition and sentiment analysis," in Data Mining (ICDM), 2016 IEEE 16th International Conference on, 2016, pp. 439-448.
  35. J. Yao, H. Wang, and P. Yin, "Sentiment feature identification from Chinese online reviews," in Advances in Information Technology and Education, ed: Springer, 2011, pp. 315-322.
  36. A. K. Dash, J. K. Rout, and S. K. Jena, "Harnessing Twitter for Automatic Sentiment Identification Using Machine Learning Techniques," in Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, 2016, pp. 507-514.
  37. L. Xie, M. Zhou, and M. Sun, "Hierarchical structure based hybrid approach to sentiment analysis of chinese micro blog and its feature extraction," Journal of Chinese information processing, vol. 26, pp. 73-82, 2012.
  38. H. Saif, Y. He, M. Fernandez, and H. Alani, "Contextual semantics for sentiment analysis of Twitter," Information Processing & Management, vol. 52, pp. 5-19, 2016
  39. P. Chen, B. Xu, M. Yang, and S. Li, "Clause sentiment identification based on convolutional neural network with context embedding," in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), 2016 12th International Conference on, 2016, pp. 1532-1538.
  40. Argamon, S., Koppel, M., & Avneri, G. (1998). Routing documents according to style.
  41. In First international workshop on innovative information systems.
  42. W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Engineering Journal, vol. 5, pp. 1093-1113, 2014.
Index Terms

Computer Science
Information Sciences

Keywords

Sentiment Analysis Sentiment Detection Sentiment Recognition Aspect identification Text Mining

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