This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.
Abd-Elhamid, L., Elzanfaly, D., & Eldin, A.S. (2016). Feature-based sentiment analysis in online Arabic reviews. In Proceedings of 11th International Conference on Computer Engineering & Systems (pp.260–265). IEEE. doi: 10.1109/ICCES.2016.7822011Abd-ElhamidL.ElzanfalyD.EldinA.S.2016Feature-based sentiment analysis in online Arabic reviews260–265IEEE10.1109/ICCES.2016.7822011Open DOISearch in Google Scholar
Akhtar, M. S, Gupta, D., & Ekbal, A. (2017). Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis. Knowledge-Based Systems, 125, 116–135. doi: 10.1016/j.knosys.2017.03.020AkhtarM. S, Gupta, D.EkbalA.2017Feature selection and ensemble construction: A two-step method for aspect based sentiment analysis12511613510.1016/j.knosys.2017.03.020Open DOISearch in Google Scholar
Asghar, M.Z., Khan, A., Ahmad, S., Qasim, M., & Khan, I. A (2017). Lexicon-enhanced sentiment analysis framework using rule-based classification scheme. PloS One, 12(2), e0171649. doi: 10.1371/journal.pone.0171649AsgharM.Z.KhanA.AhmadS.QasimM.KhanI. A2017Lexicon-enhanced sentiment analysis framework using rule-based classification scheme122e017164910.1371/journal.pone.0171649532298028231286Open DOISearch in Google Scholar
Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2016) Enriching word vectors with subword information. arXiv:1607.04606v2.BojanowskiP.GraveE.JoulinA.MikolovT.2016Enriching word vectors with subword information10.1162/tacl_a_00051Search in Google Scholar
Contratres, F.G., Alves-Souza, S.N., Filgueiras, L.V.L., & DeSouza, L.S. (2018). Sentiment analysis of social network data for cold-start relief in recommender systems. In Proceedings of World Conference on Information Systems and Technologies (pp.122–132). Springer, Cham. doi: 10.1007/978-3-319-77712-2_12ContratresF.G.Alves-SouzaS.N.FilgueirasL.V.L.DeSouzaL.S.2018Sentiment analysis of social network data for cold-start relief in recommender systems122–132Springer, Cham10.1007/978-3-319-77712-2_12Open DOISearch in Google Scholar
Endo, D., Saito, M., & Yamamoto. (2006).The extraction of emotional representation by using dependency relation. In Proceedings of Natural Language Processing.EndoD.SaitoM.Yamamoto2006The extraction of emotional representation by using dependency relationSearch in Google Scholar
Fernández, A.M., Esuli, A., & Sebastiani, F. (2016). Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification. Journal of Artificial Intelligence Research, 55(1), 131–163. doi: 10.1613/jair.4762FernándezA.M.EsuliA.SebastianiF.2016Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification55113116310.1613/jair.4762Open DOISearch in Google Scholar
Grave, E., Bojanowski, P., Gupta, P., Joulin, A., & Mikolov, T. (2018). Learning word vectors for 157 languages. In Proceedings of the International Conference on Language Resources and Evaluation (LREC 2018).GraveE.BojanowskiP.GuptaP.JoulinA.MikolovT.2018Learning word vectors for 157 languagesSearch in Google Scholar
Impana, P., & Kallimani, J.S. (2017). Cross-lingual sentiment analysis for Indian regional languages (pp.1–6). In Proceedings of International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques.ImpanaP.KallimaniJ.S.2017Cross-lingual sentiment analysis for Indian regional languages1–610.1109/ICEECCOT.2017.8284625Search in Google Scholar
Ma, W., & Deng, Y. (2013). New feature weighting calculation method for short text. Journal of Computer Applications, 33(8), 2280–2292.MaW.DengY.2013New feature weighting calculation method for short text3382280229210.3724/SP.J.1087.2013.02280Search in Google Scholar
Manek, A.S., Shenoy, P.D., Mohan, M.C., & Venugopal, K.R. (2016). Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier. World Wide Web, 20(2), 135–154. doi: 10.1007/s11280-015-0381-xManekA.S.ShenoyP.D.MohanM.C.VenugopalK.R.2016Aspect term extraction for sentiment analysis in large movie reviews using Gini Index feature selection method and SVM classifier20213515410.1007/s11280-015-0381-xOpen DOISearch in Google Scholar
Nakamura, A. (1979). Kanjo Hyogen Jiten Toukyouto Rokkou Press.NakamuraA.1979Search in Google Scholar
Omar, N., Albared, M., Al-Moslmi, T, &. Al-Shabi, A. (2014) A comparative study of feature selection and machine learning algorithms for Arabic sentiment classification. Information Retrieval Technology, 8870, 429–443. doi: 10.1007/978-3-319-12844-3_37OmarN.AlbaredM.Al-MoslmiT, &. Al-Shabi, A.2014A comparative study of feature selection and machine learning algorithms for Arabic sentiment classification887042944310.1007/978-3-319-12844-3_37Open DOISearch in Google Scholar
Parlak, B., & Uysal, A.K. (2018). On Feature weighting and selection for medical document classification. Developments and Advances in Intelligent Systems and Applications (pp. 269–282). Springer, Cham.ParlakB.UysalA.K.2018Developments and Advances in Intelligent Systems and Applications269282Springer, Cham10.1007/978-3-319-58965-7_19Search in Google Scholar
Palakvangsa-Na-Ayudhya, S, Sriarunrungreung. V, Thongprasan, P., & Porcharoen, S. (2011) Nebular: A sentiment classification system for the tourism business. In Proceedings of 2011 Eighth International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp.293–298). IEEE. doi: 10.1109/JCSSE.2011.5930137Palakvangsa-Na-AyudhyaSSriarunrungreungVThongprasanP.PorcharoenS.2011Nebular: A sentiment classification system for the tourism business293–298IEEE10.1109/JCSSE.2011.5930137Open DOISearch in Google Scholar
Palaniappan, R., Sundaraj, K., & Sundaraj, S. (2014). A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signal. BMC Bioinformatics, 15(1), 223. doi: 10.1186/1471-2105-15-223PalaniappanR.SundarajK.SundarajS.2014A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signal15122310.1186/1471-2105-15-223409499324970564Open DOISearch in Google Scholar
Severyn, A., Moschitti, A., Uryupina, O., Plank, B., & Filippova, K. (2016). Multi-lingual opinion mining on YouTube. Information Processing and Management, 52(1), 46–60. doi: 10.1016/j.ipm.2015.03.002SeverynA.MoschittiA.UryupinaO.PlankB.FilippovaK.2016Multi-lingual opinion mining on YouTube521466010.1016/j.ipm.2015.03.002Open DOISearch in Google Scholar
Sharma, A., & Dey, S. (2012). A comparative study of feature selection and machine learning techniques for sentiment analysis. In Proceedings of the 2012 ACM research in applied computation symposium (pp.1–7). ACM. doi: 10.1145/2401603.2401605SharmaA.DeyS.2012A comparative study of feature selection and machine learning techniques for sentiment analysis1–7ACM10.1145/2401603.2401605Open DOISearch in Google Scholar
Siddiqua, U.A., Ahsan, T., & Chy, A.N. (2017). Combining a rule-based classifier with weakly supervised learning for twitter sentiment analysis. In Proceedings of International Conference on Innovations in Science (pp.1–4), Engineering and Technology. doi: 10.1109/ICISET. 2016.7856499SiddiquaU.A.AhsanT.ChyA.N.2017Combining a rule-based classifier with weakly supervised learning for twitter sentiment analysis1–4Engineering and Technology10.1109/ICISET2016.7856499Open DOISearch in Google Scholar
Song, W., Cai, Y., Wu, B., & Sun, T. (2012). A new active learning strategy in nearest neighbor classifier. In Proceedings of the International Conference on Machine Learning and Cybernetics (pp.729–734). Xi’an, China. IEEE. doi: 10.1109/ICMLC.2012.6359015SongW.CaiY.WuB.SunT.2012A new active learning strategy in nearest neighbor classifier729–734Xi ’ an, China. IEEE10.1109/ICMLC.2012.6359015Open DOISearch in Google Scholar
Soni A K. (2017). Multi-lingual sentiment analysis of Twitter data by using classification algorithms. In Proceedings of 2017 Second International Conference on Electrical, Computer and Communication Technologies (ICECCT) (pp.1–5). doi: 10.1109/ICECCT.2017.8117884SoniA K.2017Multi-lingual sentiment analysis of Twitter data by using classification algorithmsICECCT1–510.1109/ICECCT.2017.8117884Open DOISearch in Google Scholar
Vulic, I., Smet, W.D., Tang, J., & Moens, MF. (2015). Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications. Information Processing & Management 51(1), 111–147. doi: 10.1016/j.ipm.2014.08.003VulicI.SmetW.D.TangJ.MoensMF.2015Probabilistic topic modeling in multilingual settings: An overview of its methodology and applications51111114710.1016/j.ipm.2014.08.003Open DOISearch in Google Scholar
Xia, R., Xu, F., Yu, J., Qi, Y. & Cambria, E (2016). Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis. Information Processing & Management, 52(1), 36–45. doi: 10.1016/j.ipm.2015.04.003XiaR.XuF.YuJ.QiY.CambriaE2016Polarity shift detection, elimination and ensemble: A three-stage model for document-level sentiment analysis521364510.1016/j.ipm.2015.04.003Open DOISearch in Google Scholar
Xiao, X., Lu, J., Yu, L., & Gong, H. (2015). Research on feature selection algorithm based on the lowest term frequency of CHI. Journal of Southwest University (Natural Science Edition), 37(6), 137–142.XiaoX.LuJ.YuL.GongH.2015Research on feature selection algorithm based on the lowest term frequency of CHI376137142Search in Google Scholar
Xu, F.Y., & Luo, Z.S. (2015). An improved approach to term weighting in automated text classification. Computer Engineering and Application, 4(1), 181–184.XuF.Y.LuoZ.S.2015An improved approach to term weighting in automated text classification41181184Search in Google Scholar
Yang, W., Song, J.J., & Tang, J.Q. (2013). A study on the classification approach for Chinese MicroBlog subjective and objective sentences. Journal of Chongqing University of Technology (Natural Science), 27(1), 51–56.YangW.SongJ.J.TangJ.Q.2013A study on the classification approach for Chinese MicroBlog subjective and objective sentences2715156Search in Google Scholar
Yang, Y.M., & Pedersen, J.O. (1997). A comparative study on feature selection in text categorization. In Proceedings of the 14th International Conference on Machine Learning (pp. 412–420). Nashville, TN, USA.YangY.M.PedersenJ.O.1997A comparative study on feature selection in text categorization412420Nashville, TN, USASearch in Google Scholar
Zhang, C.Z., & Zhou, Q.Q. (2018). Online investigation of users’ attitudes using automatic question answering. Online Information Review, 2018, 42(3), 419–435. doi: 10.1108/OIR-10-2016-0299ZhangC.Z.ZhouQ.Q.2018Online investigation of users’ attitudes using automatic question answering42341943510.1108/OIR-10-2016-0299Open DOISearch in Google Scholar
Zhang, L. (2015) Aspect: eight summary of “Internet + tourism” industry trend in 2016. Retrieved from http://mi.chinabyte.com/299/13641299.htmlZhangL.2015Retrieved fromhttp://mi.chinabyte.com/299/13641299.htmlSearch in Google Scholar
Zhang, L., Jiang, L., Li, C., & Kong, G. (2016). Two feature weighting approaches for naive Bayes text classifiers. Knowledge-Based Systems, 100, 137–144. doi: 10.1016/j.knosys.2016.02.017ZhangL.JiangL.LiC.KongG.2016Two feature weighting approaches for naive Bayes text classifiers10013714410.1016/j.knosys.2016.02.017Open DOISearch in Google Scholar
Zheng, L., Wang, H., & Gao, S. (2015). Sentimental feature selection for sentiment analysis of Chinese online. International Journal of Machine Learning and Cybernetics, 9(1), 75–84.ZhengL.WangH.GaoS.2015Sentimental feature selection for sentiment analysis of Chinese online91758410.1007/s13042-015-0347-4Search in Google Scholar
Zhou, G.Y., Zhu Z.Y., He, T.T., & Hu, X.T. (2016). Cross-lingual sentiment classification with stacked auto-encoders. Knowledge and Information Systems, 47(1), 27–44. doi: 10.1007/s10115-015-0849-0ZhouG.Y.ZhuZ.Y.HeT.T.HuX.T.2016Cross-lingual sentiment classification with stacked auto-encoders471274410.1007/s10115-015-0849-0Open DOISearch in Google Scholar
Zin, H.M., Mustapha, N., Murad, M.A.A. & Sharef, N.M. (2018). Term weighting scheme effect in sentiment analysis of online movie reviews. Advanced Science Letters, 24(2), 933–937.ZinH.M.MustaphaN.MuradM.A.A.SharefN.M.2018Term weighting scheme effect in sentiment analysis of online movie reviews24293393710.1166/asl.2018.10661Search in Google Scholar