Special Issue: DOMAIN ADAPTABLE MODEL FOR SENTIMENT ANALYSIS, 81-86.

Vaishali Kalra, Dr. Rashmi Agrawal, and Srishti Sharma

References

  1. [1] R. Sharma, P. Bhattacharyya, S. Dandapat, and H.S. Bhatt,Identifying transferable information across domains for cross-domain sentiment classification, in ACL 2018 - 56th Annu.Meet. Assoc. Comput. Linguist. Proc. Conf. (Long Pap. 1,968–978 (2018). https://doi.org/10.18653/v1/p18-1089.
  2. [2] D. Bollegala, D. Weir, and J. Carroll, Using multiple sourcesto construct a sentiment sensitive thesaurus for cross-domainsentiment classification, ACL-HLT 2011 – Proceedings of 9thAnnual Meeting of the Association for Computational Linguis-tics: Human Language Technologies, vol. 1 (2011), 132–141.
  3. [3] L. Wang, J. Niu, H. Song, and M. Atiquzzaman, Sen-tiRelated: A cross-domain sentiment classification algorithmfor short texts through sentiment related index, Journal ofNetwork and Computer Applications 101, 2018, 111–119.https://doi.org/10.1016/j.jnca.2017.11.001.
  4. [4] X. Wan, Co-Training for Cross-Lingual Sentiment Classifica-tion (2009), 235–243.
  5. [5] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede,Lexicon-Based Methods for Sentiment Analysis (2011).
  6. [6] M.Z. Asghar, A. Khan, S. Ahmad, M. Qasim, and I.A.Khan, Lexicon-enhanced sentiment analysis framework usingrule-based classification scheme, PLoS One, 12, 2017 , 1–22.https://doi.org/10.1371/journal.pone.0171649.
  7. [7] M. Liu, Y. Song, H. Zou, and T. Zhang, Reinforced TrainingData Selection for Domain Adaptation (2019), 1957–1968.https://doi.org/10.18653/v1/p19-1189.
  8. [8] O. Araque, I. Corcuera-Platas, J.F. Snchez-Rada, and C.A.Iglesias, Enhancing deep learning sentiment analysis withensemble techniques in social applications, Expert Syst. Appl.77, 2017, 236–246. https://doi.org/10.1016/j.eswa.2017.02.002.
  9. [9] C. Banea, R. Mihalcea, and J. Wiebe, A Bootstrapping Methodfor Building Subjectivity Lexicons for Languages with ScarceResources.
  10. [10] C. J. Hutto and E. Gilbert, VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text(2014).
  11. [11] S. Baccianella, A. Esuli, and F. Sebastiani, SENTIWORDNET3.0: An Enhanced Lexical Resource for Sentiment Analysisand Opinion Mining.
  12. [12] C. Zhao, S. Wang, and D. Li, Multi-source domain adap-tation with joint learning for cross-domain sentiment clas-sification, Knowledge-Based Systems, 191, 2020, 105254.https://doi.org/10.1016/j.knosys.2019.105254.
  13. [13] A. Deshwal and S. K. Sharma, Twitter sentiment analysis us-ing various classification algorithms, in 2016 5th InternationalConference on Reliability, Infocom Technologies and Optimiza-tion, ICRITO 2016: Trends and Future Directions (Instituteof Electrical and Electronics Engineers Inc., 2016), 251–257.https://doi.org/10.1109/ICRITO.2016.7784960.
  14. [14] C. Hung and S.J. Chen, Word sense disambigua-tion based sentiment lexicons for sentiment classifica-tion, Knowledge-Based Systems 110, 2016, 224–232.https://doi.org/10.1016/j.knosys.2016.07.030.
  15. [15] H. Han, J. Zhang, J. Yang, Y. Shen, and Y.Zhang, Generate domain-specific sentiment lexicon for re-view sentiment analysis, Multimedia Tools and Applica-tions, https://dl.acm.org/doi/10.5555/3269988.3270093, lastaccessed 2020/03/17.
  16. [16] H. Keshavarz and M.S. Abadeh, ALGA: Adaptive lexi-con learning using genetic algorithm for sentiment analysisof microblogs, Knowledge-Based Systems, 122, 2017, 1–16.https://doi.org/10.1016/j.knosys.2017.01.028.
  17. [17] A. Tripathy, A. Agrawal, and S.K. Rath, Classificationof sentiment reviews using n-gram machine learning ap-proach, Expert Systems With Applications, 57, 2016, 117–126.https://doi.org/10.1016/j.eswa.2016.03.028.
  18. [18] M. Yang, D. Zhu, R. Mustafa, and K.P. Chow, Learningdomain-specific sentiment lexicon with supervised sentiment-aware LDA, Frontiers in Artificial Intelligence and Appli-cations, 263, 2014, 927–932. https://doi.org/10.3233/978-1-61499-419-0-927.
  19. [19] D. Bollegala, D. Weir, and J. Carroll, Cross-domain senti-ment classification using a sentiment sensitive thesaurus, IEEETransactions on Knowledge and Data Engineering, 25, 2013,1719–1731. https://doi.org/10.1109/TKDE.2012.103.
  20. [20] S.J. Pan, X. Ni, J.T. Sun, Q. Yang, and Z. Chen, Cross-domain sentiment classification via spectral feature alignment,in Proc. 19th Int. Conf. World Wide Web, WWW 2010 (2010),751–760. https://doi.org/10.1145/1772690.1772767.
  21. [21] A.L. Maas, R.E. Daly, P.T. Pham, D. Huang, A.Y. Ng, andC. Potts, Learning Word Vectors for Sentiment Analysis.
  22. [22] J. Blitzer, R. Mcdonald, and F. Pereira, Domain Adaptationwith Structural Correspondence Learning (2006).
  23. [23] Y. Bao, N. Collier, and A. Datta, A partially super-vised cross-collection topic model for cross-domain textclassification, in International Conference on Informationand Knowledge Management, Proceedings, 2013, 239–248.https://doi.org/10.1145/2505515.2505556.
  24. [24] M. Ghiassi and S. Lee, A domain transferable lexicon setfor Twitter sentiment analysis using a supervised machinelearning approach, Expert Systems With Applications, 106,2018, 197–216. https://doi.org/10.1016/j.eswa.2018.04.006.
  25. [25] M. Al-Smadi, O. Qawasmeh, M. Al-Ayyoub, Y. Jararweh, andB. Gupta, Deep recurrent neural network vs. support vectormachine for aspect-based sentiment analysis of Arabic hotels’reviews, Journal of Computational Science, 27, 2018, 386–393.https://doi.org/10.1016/j.jocs.2017.11.006.
  26. [26] Y. Zhang, L. Shang, and X. Jia, Sentiment analysis on mi-croblogging by integrating text and image features, in LectureNotes in Computer Science (including subseries Lecture Notesin Artificial Intelligence and Lecture Notes in Bioinformatics)(Springer Verlag, 2015), 52–63 https://doi.org/10.1007/978-3-319-18032-8 5.
  27. [27] M.Z. Asghar, A. Khan, S. Ahmad, and F.M. Kundi, A reviewof feature extraction in sentiment analysis, Journal of Basicand Applied Scientific Research, 4, 2014, 181–186.
  28. [28] U. Naseem, S.K. Khan, I. Razzak, and I.A. Hameed, Hybridwords representation for airlines sentiment analysis, in LectureNotes in Computer Science (including subseries Lecture Notesin Artificial Intelligence and Lecture Notes in Bioinformatics)(Springer, 2019), 381–392. https://doi.org/10.1007/978-3-030-35288-2 31.
  29. [29] J. Soni, K. Mathur, and Y.S. Patsariya, “Performance im-provement of Nave Bayes classifier for sentiment estimationin ambiguous tweets of US Airlines, in Advances in Intel-ligent Systems and Computing (Springer, 2020), 195–204.https://doi.org/10.1007/978-981-15-1097-7 17.
  30. [30] A. Rane and A. Kumar, Sentiment classification systemof twitter data for US airline service analysis, in Pro-ceedings - International Computer Software and Applica-tions Conference (IEEE Computer Society, 2018), 769–773.https://doi.org/10.1109/COMPSAC.2018.00114.85
  31. [31] M. Vadivukarassi, N. Puviarasan, and P. Aruna, A com-parison of supervised machine learning approaches for cat-egorized tweets, in Lecture Notes on Data Engineeringand Communications Technologies (Springer, 2019), 422–430.https://doi.org/10.1007/978-3-030-03146-6 47.
  32. [32] S. Kiritchenko, Sentiment Analysis of Short Informal Texts,50, 2014, 723–762.
  33. [33] B. Xiang, Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training (2014),434–439.
  34. [34] N.F.F. Da Silva, L.F.S. Coletta, E.R. Hruschka, andE.R. Hruschka, Using unsupervised information to im-prove semi-supervised tweet sentiment classification, In-formation Sciences (NY), 355–356, 2016, 348–365.https://doi.org/10.1016/j.ins.2016.02.002.
  35. [35] G. Hu and Z. Du, Adaptive fuzzy control by real-time choos-ing multi-model architecture for uncertain nonlinear system,System, 1, 2019, 12.

Important Links:

Go Back