A hybrid semi-supervised boosting to sentiment analysis

[1] U. Aggarwal and G. Aggarwal. Sentiment analysis: A survey. Int. J. Comput. Sci. Engin. 5(5), (2017) 222–225.
[2] M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E. D. Trippe, J. B. Gutierrez, and K. Kochut. A brief survey of
text mining: Classification, clustering and extraction techniques. arXiv preprint arXiv:1707.02919, 2017.
[3] J. A. Balazs and J. D. Vel´asquez. Opinion mining and information fusion: a survey. Inf. Fus. 27, (2016) 95–110.
[4] M. Belkin, P. Niyogi, and V. Sindhwani. Manifold regularization: A geometric framework for learning from labeled
and unlabeled examples. J. Machine Learn. Res. 7, (2006) 2399–2434.
[5] S. Bhatia, M. Sharma, and K. K. Bhatia. Sentiment analysis and mining of opinions. 503–523. Springer, 2018.[6] P. Biyani, C. Caragea, P. Mitra, C. Zhou, J. Yen, G. E. Greer, and K. Portier. Co-training over domainindependent and domain-dependent features for sentiment analysis of an online cancer support community. Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on, 2013,
413–417.
[7] A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training. In Proc. Eleventh Annual
Conf. Comput. Learn. Theory, (1998) 92–100.
[8] K. Chen and S. Wang. Semi-supervised learning via regularized boosting working on multiple semi-supervised
assumptions. IEEE Trans. Pattern Anal. Machine Intel. 33(1), (2011) 129–143.
[9] K. Crammer, A. Kulesza, and M. Dredze. Adaptive regularization of weight vectors. Adv. Neural Inf. Proc. Syst.
2009, (2009) 414–422.
[10] D. Davidov, O. Tsur, and A. Rappoport. Enhanced sentiment learning using twitter hashtags and smileys. Proc.
23rd Int. Conf. Comput. Ling. Posters, 2010, 241–249.
[11] W. C. Dhaoui, C. and L. Tan. Social media sentiment analysis: lexicon versus machine learning. J. Consumer
Market. 39(6), (2017) 480–488.
[12] J. D’hondt, J. Vertommen, P. Verhaegen, D. Cattrysse, and J. R. Duflou. Pairwise-adaptive dissimilarity measure
for document clustering. Inf. Sci. 180(12), (2010) 2341–2358.
[13] Y. Han, Y. Liu, and Z. Jin. Sentiment analysis via semi-supervised learning: a model based on dynamic threshold
and multi-classifiers. Neural Computing and Applications, 32, 2020, 5117–5129.
[14] S. Hong, J. Lee, and J.-H. Lee. Competitive self-training technique for sentiment analysis in mass social media.
Soft Comput. Intel. Syst. 2014 Joint 7th Int. Conf. Adv. Intel. Syst. 15th Int. Symp. 2014, (2014) 9–12.
[15] M. Hu and B. Liu. Mining and summarizing customer reviews. Proc. Tenth ACM SIGKDD Int. Conf. Knowledge
Disc. Data Min., (2004) 168–177.
[16] X. Hu, L. Tang, J. Tang, and H. Liu. Exploiting social relations for sentiment analysis in microblogging. Proc.
Sixth ACM Int. Conf. Web Search Data Min., 2013, 537–546.
[17] S. Inzalkar and J. Sharma. A survey on text mining-techniques and application. Int. J. Res. Sci. Engin. 24, (2015)
1–14.
[18] F. H. Khan, U. Qamar, and S. Bashir. A semi-supervised approach to sentiment analysis using revised sentiment
strength based on sentiwordnet. Knowl. Inf. Syst. 51(3), (2017) 851–872.
[19] S. Kumar, K. De, and P. P. Roy. Movie recommendation system using sentiment analysis from microblogging
data. IEEE Trans. Comput. Soc. Syst. 2020, (2020) 1–9.
[20] M. Labani, P. Moradi, F. Ahmadizar, and M. Jalili. A novel multivariate filter method for feature selection in
text classification problems. Engin. Appl. Artif. Intel. 70, (2018) 25–37.
[21] Z. Li, C. Li, L. Yang, P. S. Yu, and Z. Li. Mixture distribution modeling for scalable graph-based semi-supervised
learning. Knowledge-Based Syst. 200, (2020) 105974.
[22] Y. Lin, J. Jiang, and S. Lee. A similarity measure for text classification and clustering. IEEE Trans. Knowledge
Data Engin. 26(7), (2014) 1575–1590.
[23] S. Liu, W. Zhu, N. Xu, F. Li, X.-q. Cheng, Y. Liu, and Y. Wang. Co-training and visualizing sentiment evolvement
for tweet events. Proc. 22nd Int. Conf. World Wide Web, 2013, 105–106.
[24] W. Liu, X. Jing, Y. Chen, and J. Li. Co-training based on multi-type text features. Int. Conf. Signal Inf. Proc.
Network. Comput., 2017, 213–220.
[25] Z. Liu, X. Dong, Y. Guan, and J. Yang. Reserved self-training: A semi-supervised sentiment classification method
for chinese microblogs. Proc. Sixth Int. Joint Conf. Natural Lang. Proc., 2013, 455–462.
[26] Z. Miao, Y. Li, X. Wang, and W. Tan. Snippext: Semi-supervised opinion mining with augmented data. CoRR,
abs/2002.03049, 2020.
[27] S. M. Mohammad, S. Kiritchenko, and X. Zhu. Nrc-canada: Building the state-of-the-art in sentiment analysis
of tweets. arXiv preprint arXiv:1308.6242, 2013.
[28] A. Pak and P. Paroubek. Twitter based system: Using twitter for disambiguating sentiment ambiguous adjectives.
Proc. 5th Int. Workshop Semantic Ev., 2010, 436–439.
[29] S. Park, J. Lee, and K. Kim. Semi-supervised distributed representations of documents for sentiment analysis.
Neural Networks, 119, (2019) 139–150.
[30] L. Qiu, W. Zhang, C. Hu, and K. Zhao. Selc: a self-supervised model for sentiment classification. Proc. 18th
ACM conf. Inf. Knowledge Manag., 2009, 929–936.
[31] J. Read and J. Carroll. Weakly supervised techniques for domain-independent sentiment classification. Proc. 1st
Int. CIKM Workshop Topic-sentiment Anal. Mass Opin., 2009, 45–52.
[32] H. Saif, T. Dickinson, L. Kastler, M. Fernandez, and H. Alani. A semantic graph-based approach for radicalisation
detection on social media. Euro. Semantic web Conf., 2017, 571–587.[33] J. Serrano-Guerrero, J. A. Olivas, F. P. Romero, and E. Herrera-Viedma. Sentiment analysis: A review and
comparative analysis of web services. Inf. Sci. 311, (2015) 18–38.
[34] N. F. F. Silva, L. F. Coletta, E. R. Hruschka, and E. R. Hruschka Jr. Using unsupervised information to improve
semi-supervised tweet sentiment classification. Inf. Sci. 355, (2016) 348–365.
[35] N. F. F. D. Silva, L. F. Coletta, and E. R. Hruschka. A survey and comparative study of tweet sentiment analysis
via semi-supervised learning. ACM Computing Surveys, 49(1), (2016) 1–15.
[36] K. Taghva, R. Beckley, and M. Sadeh. A list of farsi stopwords. Ret. Sept. 2003(7), (2003).
[37] C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li. User-level sentiment analysis incorporating social networks.
Proc. 17th ACM SIGKDD Int. Conf. Knowledge Disc. Data Min., 2011, 1397–1405.
[38] J. Tanha. Mssboost: A new multiclass boosting to semi-supervised learning. Neurocomput. 2018, (2018).
[39] J. Tanha. A multiclass boosting algorithm to labeled and unlabeled data. Int. J. Machine Learn. Cyber. 2019,
(2019).
[40] J. Tanha, M. J. Saberian, and M. Van Someren. Multiclass semi-supervised boosting using similarity learning.
Data Mining (ICDM), 2013 IEEE 13th Int. Conf., 2013, 1205–1210.
[41] J. Tanha, M. Van Someren, and H. Afsarmanesh. Boosting for multiclass semi-supervised learning. Pattern
Recog. Let. 37, (2014) 63–77.
[42] J. Tanha, M. van Someren, and H. Afsarmanesh. Semi-supervised self-training for decision tree classifiers. Int.
J. Machine Learn. Cyber. 8(1), (2017) 355–370.
[43] H. Thakkar and D. Patel. Approaches for sentiment analysis on twitter: A state-of-art study. arXiv preprint
arXiv:1512.01043, 2015.
[44] A. Tripathy, A. Agrawal, and S. K. Rath. Classification of sentiment reviews using n-gram machine learning
approach. Expert Syst. Appl. 57, (2016) 117–126.
[45] H. Valizadegan, R. Jin, and A. K. Jain. Semi-supervised boosting for multi-class classification. Joint Euro. Conf.
Machine Learn. Knowledge Disc. Datab. 2008, (2008) 522–537.
[46] B. Xiang and L. Zhou. Improving twitter sentiment analysis with topic-based mixture modeling and semi-supervised
training. Proc. 52nd Annual Meet. Assoc. Comput. Ling. 2, (2014) 434–439.
[47] W. Xu and Y. Tan. Semi-supervised target-oriented sentiment classification. Neurocomput. 337, (2019) 120–128.
[48] N. Yu. Exploring c o-training strategies for opinion detection. J. Assoc. Inf. Sci. Tech. 56(10), (2014) 2098–2110.
[49] N. Yu and S. Kubler. Semi-supervised learning for opinion detection. Web Intel. Intel. Agent Tech. (WI-IAT),
2010 IEEE/WIC/ACM International Conf. 3, (2010) 249–252.
[50] T. Zagibalov and J. Carroll. Unsupervised classification of sentiment and objectivity in chinese text. Proc. Third
Int. Joint Conf. Natural Lang. Proc. Volume-I, 2008.
[51] S. Zeng, D. Luo, C. Zhang, and X. Li. A Correlation-Based TOPSIS Method for Multiple Attribute Decision
Making with Single-Valued Neutrosophic Information. Int. J. Inf. Tech. Dec. Mak. 19(1), ().
[52] J. Zhao, M. Lan, and T. Zhu. Ecnu: Expression-and message-level sentiment orientation classification in twitter
using multiple effective features. Proc. 8th Intm Workshop Semantic Ev. 2014, (2014) 259–264.
[53] F. Zou, F. L. Wang, X. Deng, and S. Han. Automatic identification of chinese stop words. Res. Comput. Sci. 18,
(2006) 151–162.