A survey on sentiment classification algorithms, challenges and applications

[1] M. Aminu, Contextual lexicon-based sentiment analysis for social media, PhD Thesis, Université Robert Gordon University, Aberdeen, 2016. ⇒ 59Search in Google Scholar

[2] A. Andreevskaia, S. Bergler, When specialists and generalists work togethe over-coming domain dependence in sentiment tagging, Proc. ACL08 HLT, 2008, pp.290–298. ⇒ 63Search in Google Scholar

[3] A. Andronic, F. Arleo, R. Arnaldi, A. Beraudo, E. Bruna, D. Caffarri, Z. Conesa del Valle et al., Heavy-flavour and quarkonium production in the lhc era: from protonproton to heavy-ion collisions, The European Physical Journal (2016) 76: 107. ⇒ 6710.1140/epjc/s10052-015-3819-5494686927471429Search in Google Scholar

[4] R. N. Behera, R. Manan, S. Dash, Ensemble based hybrid machine learning approach for sentiment classification – A Review, International Journal of Computer Applications146, 6 (2016) 31–36. ⇒ 5910.5120/ijca2016910813Search in Google Scholar

[5] S. Brody, N. Diakopoulos, Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs, Conference on Empirical Methods in Natural Language Processing, 2007, pp. 562–570. ⇒ 64Search in Google Scholar

[6] Y. Choi, H.Lee, Data properties and the performance of sentiment classification for electronic commerce applications, Information Systems Frontiers19 (2017) 1–20. ⇒ 6810.1007/s10796-017-9741-7Search in Google Scholar

[7] K. T. Devendra, S. K. Yadav, Fast retrieval approach of sentimental analysis with implementation of bloom filter on Hadoop, International Conference on Computational Techniques in Information and Communication Technologies, 2016, pp.529–551. ⇒ 59Search in Google Scholar

[8] L. Dey, S. Chakraborty, A. Beepa, S. Tiwari, Sentiment analysis of review datasets Using nave bayes and k-nn classifier, Information Engineering and Electronic Business (2016) 54–62. ⇒ 6510.5815/ijieeb.2016.04.07Search in Google Scholar

[9] X. Ding, B. Liu, P. S. Yu, A holistic lexicon-based approach to opinion mining, Proc. of the 2008 international conference on web search and data mining, ACM, 2008, pp. 231–240. ⇒ 6410.1145/1341531.1341561Search in Google Scholar

[10] A. Esuli, F. Sebastiani, Determining term subjectivity and term orientation for opinion mining, 11th Conference of the European Chapter of the Association for Computational Linguistics, 2006. ⇒ 63Search in Google Scholar

[11] Y. Fei, Simultaneous support vector selection and parameter optimization using support vector machines for sentiment classification, Software Engineering and Service Science (ICSESS), 7th IEEE Int. Conference, 2016, pp. 59–62. ⇒ 5910.1109/ICSESS.2016.7883015Search in Google Scholar

[12] S. Feng, R. Bose, Y. Choi, Connotation lexicon: A dash of sentiment beneath the surface meaning, Proc. 51st Annual Meeting of the Association for Computational Linguistics, 2013, pp-1774–1784. ⇒ 64Search in Google Scholar

[13] V. Hatzivassiloglou, K. R. McKeown, Predicting the semantic orientation of adjectives, Proc. 35th Annual Meeting of the Association for Computational Linguistics, 1997, pp. 174–181. ⇒ 6310.3115/976909.979640Search in Google Scholar

[14] W. Haywood, J. Ricky, J. B. Holcomb, E. A. Gonzalez, Z. Peng, S. Pati, P. W. Park, W. Wang, A. M. Zaske, T. Menge, R. A. Kozar, Modulation of syndecan-1 shedding after hemorrhagic shock and resuscitation, PloS, 2011. ⇒ 63Search in Google Scholar

[15] W. He, X. Tian, R. Tao, W. Zhang, G. Yan, V. Akula, Application of social media analytics: a case of analyzing online hotel reviews, Online Information Review (2017) 921–935. ⇒ 6510.1108/OIR-07-2016-0201Search in Google Scholar

[16] D. T. Hess, Akio Matsumoto, Sung-Oog Kim, H. E. Marshall, J. S. Stamler, Protein s-nitrosylation: purview and parameters, Nature Reviews Molecular Cell Biology (2005). ⇒ 6710.1038/nrm156915688001Search in Google Scholar

[17] T. Hofmann, Probabilistic latent semantic indexing, Proc. 16th Int. Conference on World Wide Web, 1999, pp. 50–57. ⇒ 66Search in Google Scholar

[18] W. Jin, H. H. Ho, R. K. Srihari, Opinionminer: a novel machine learning system for web opinion mining and extraction, Proc. 15th ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining, 2009, pp. 1195–1204. ⇒ 6710.1145/1557019.1557148Search in Google Scholar

[19] N. Kaji, M. Kitsuregawa, Building lexicon for sentiment analysis from massive collection of html documents, Proc. of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), 2007. ⇒ 64Search in Google Scholar

[20] J. Kamps, M. Marx, R. J. Mokken, M. D. Rijkel, Using wordnet to measure semantic orientations of adjectives, LREC, 2004, pp. 1115–1118. ⇒ 63Search in Google Scholar

[21] H. Kanayama, T. Nasukawa, Fully automatic lexicon expansion for domain-oriented sentiment analysis, Proc. 2006 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2006, pp. 355–363. ⇒ 6410.3115/1610075.1610125Search in Google Scholar

[22] A. Mudinas, D. Zhang, M. Levene, Combining lexicon and learning based approaches for concept-level sentiment analysis, Proc. of the First Int. Workshop on Issues of Sentiment Discovery and Opinion Mining, 2012, pp. 51–58. ⇒ 6610.1145/2346676.2346681Search in Google Scholar

[23] S. Li, C. R. Huang, G. Zhou, S. Y. M. Lee, Employing personal/impersonal views in supervised and semi-supervised sentiment classification, Proc. 48th Annual Meeting of the Association for Computational Linguistics, 2010, pp. 414–423. ⇒ 66Search in Google Scholar

[24] F. Li, M. Huang, X. Zhu, Sentiment analysis with global topics and local dependency, Association for the Advancement of Artificial Intelligence10 (2010) 1371–1376. ⇒ 6610.1609/aaai.v24i1.7523Search in Google Scholar

[25] C. Lin, Y. H. Lee, Joint sentiment/topic model for sentiment analysis, Proc. 18th ACM Conference on Information and Knowledge Management, 2009, pp. 375–384. ⇒ 6610.1145/1645953.1646003Search in Google Scholar

[26] B. Liu, E. Blasch, Y. Chen, D. Shen, G. Chen, Scalable sentiment classification for big data analysis using naive bayes classifier, IEEE Int. Conference on Big Data, 2013, pp. 99–104. ⇒ 6510.1109/BigData.2013.6691740Search in Google Scholar

[27] B. Liu, H. Minqing, C. Junsheng, Opinion observer: analyzing and comparing opinions on the Web, Proc. 14th Int. Conference on World Wide Web, 2005, pp. 342–351. ⇒ 6710.1145/1060745.1060797Search in Google Scholar

[28] A. Manek, P. Deepa, C. Mohan, K.Venugopal, Aspect term extraction for sentiment analysis in large movie reviews using gini index feature selection method and svm classifier, World wide web20 (2016). ⇒ 61, 6510.1007/s11280-015-0381-xSearch in Google Scholar

[29] C. Mate, Product aspect ranking using sentiment analysis: a survey, Int. Research Journal of Engineering and Technology (2014). ⇒ 61Search in Google Scholar

[30] Q. Mei, X. Ling, M. Wondra, H. Su, C. Zhai, Topic sentiment mixture: modeling facts and opinions in weblogs, Proc. 16th Int. Conference on World Wide Web, 2007, pp. 171–180. ⇒ 6610.1145/1242572.1242596Search in Google Scholar

[31] P. Melville, W. Gryc, R. D. Lawrence, Sentiment analysis of blogs by combining lexical knowledge with text classication, Proc. 15th ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining, 2012, pp. 163–173. ⇒ 67Search in Google Scholar

[32] G. M. Miller, Wordnet: a lexical database for english, Communications of the Association for Computing Machinery (1995) 39–41. ⇒ 6310.1145/219717.219748Search in Google Scholar

[33] A. Ortigosa, J. Martin, R. Carro, Sentiment analysis in facebook and its application to e-learning, Computers in Human Behavior31 (2014) 527–541. ⇒ 6810.1016/j.chb.2013.05.024Search in Google Scholar

[34] B. Pang, L. Lee, S. Vaithyanathan, Thumbs up?: sentiment classification using machine learning techniques, Proc. ACL-02 Conference on Empirical Methods in Natural Language Processing, 2002, pp. 79–86. ⇒ 64, 6710.3115/1118693.1118704Search in Google Scholar

[35] R. Prabowo, M. Thelwal, Sentiment analysis: a combined approach, Journal of Informetrics3 (2009) 143–157. ⇒ 6610.1016/j.joi.2009.01.003Search in Google Scholar

[36] M. R. R. Rana, M. A. Akbar, T. Ahmad, Sentiment classification of customer reviews using bayesian classifier, Asian Journal of Engineering, Sciences & Technology7 (2017). ⇒ 65Search in Google Scholar

[37] H. Shaziya, G. Kavitha, R. Zaheer, Text categorization of movie reviews for sentiment analysis, Int. Journal of Innovative Research in Science, Engineering and Technology (2015) 11255–11262. ⇒ 65Search in Google Scholar

[38] S. P. Sivasubramanian, N. Suganya, Sentiment analysis on micro-blogs, Int. Innovative Research Journal of Engineering and Technology2 (2017) 46–51. ⇒ 59Search in Google Scholar

[39] G. Somprasertsri, P. Lalitrojwong, Mining feature-opinion in online customer reviews for opinion summarization, J. UCS16 (2010) 938–955. ⇒ 67Search in Google Scholar

[40] J. Steinberger, T. Brychcin, M. Konkol, Sentiment and social media analysis, Proc. 5th Workshop on Computational Approaches to Subjectivity, 2014. ⇒ 59Search in Google Scholar

[41] B. N. Supriya, V. Kallimani, S. Prakash, C. B. Akki, Twitter sentiment analysis using binary classification technique, Int. Conference on Nature of Computation and Communication, 2016, pp. 391–396. ⇒ 5910.1007/978-3-319-46909-6_36Search in Google Scholar

[42] M. Thelwall, K. Buckley, G. Paltoglou, Sentiment strength detection for the social web, Journal of the Association for Information Science and Technology63 (2012) 163–173. ⇒ 6710.1002/asi.21662Search in Google Scholar

[43] P. D. Turney, M. L. Littman, Measuring praise and criticism: inference of semantic orientation from association, ACM Transactions on Information Systems (TOIS) (2003) 315–346. ⇒ 6310.1145/944012.944013Search in Google Scholar

[44] T. Wilson, P. Hoffmann, S. Somasundaran, J. Kessler, Opinionfinder: a system for subjectivity analysis, HLT-Demo ’05 Proc. of HLT/EMNLP on Interactive Demonstrations, 2005, pp. 34–35. ⇒ 6710.3115/1225733.1225751Search in Google Scholar

[45] Q. Ye, Z. Zhang, R. Law, Sentiment classification of online reviews to travel destinations by supervised machine learning approaches, Expert systems with applications36 (2009) 6527–6535. ⇒ 6410.1016/j.eswa.2008.07.035Search in Google Scholar

[46] N. Zainuddin, A. Selamat, V. Kekan, Sentiment analysis using support vector machine, Computer, Communications, and Control Technology (I4CT), 2014 Int. Conference, 2014, pp. 333–337. ⇒ 5910.1109/I4CT.2014.6914200Search in Google Scholar

[47] A. Zubiaga, I. S. Vicente, P. Gamallo, J. R. P. Campos, I. A. Loinaz, N. Aranberri, A. Ezeiza, V. F. Fernandez, Overview of tweetlid: tweet language identification, TweetLID SEPLN, 2014, pp. 1–11. ⇒ 59Search in Google Scholar