Transportation sentiment analysis using word embedding and ontology-based topic modeling
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Kyung Sup Kwak | Sana Ullah | Pervez Khan | Amjad Ali | Shaker H. Ali El-Sappagh | Farman Ali | Daehan Kwak | Kyehyun Kim | Farman Ali | Daehan Kwak | Pervez Khan | K. Kwak | Shaker El-Sappagh | S. Ullah | Amjad Ali | Kyehyun Kim
[1] Zhang Hao,et al. Research and Application on Domain Ontology Learning Method Based on LDA , 2017, J. Softw..
[2] Long Chen,et al. Weakly-Supervised Deep Embedding for Product Review Sentiment Analysis , 2018, IEEE Transactions on Knowledge and Data Engineering.
[3] Mauro Dragoni,et al. A Neural Word Embeddings Approach for Multi-Domain Sentiment Analysis , 2017, IEEE Transactions on Affective Computing.
[4] Saif Mohammad,et al. NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets , 2013, *SEMEVAL.
[5] Rui Zhao,et al. Fuzzy Bag-of-Words Model for Document Representation , 2018, IEEE Transactions on Fuzzy Systems.
[6] Jianxin Li,et al. Incremental term representation learning for social network analysis , 2017, Future Gener. Comput. Syst..
[7] Dongli Yue,et al. Traffic Accidents Knowledge Management Based on Ontology , 2009, 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.
[8] Francisco C. Pereira,et al. Why so many people? Explaining Nonhabitual Transport Overcrowding With Internet Data , 2015, IEEE Transactions on Intelligent Transportation Systems.
[9] Mehran Kamkarhaghighi,et al. Content Tree Word Embedding for document representation , 2017, Expert Syst. Appl..
[10] Kyung Sup Kwak,et al. Opinion mining based on fuzzy domain ontology and Support Vector Machine: A proposal to automate online review classification , 2016, Appl. Soft Comput..
[11] Raymond Y. K. Lau,et al. Social analytics: Learning fuzzy product ontologies for aspect-oriented sentiment analysis , 2014, Decis. Support Syst..
[12] Xinyu Dai,et al. Topic2Vec: Learning distributed representations of topics , 2015, 2015 International Conference on Asian Language Processing (IALP).
[13] Miguel Ángel Rodríguez-García,et al. Ontology-based annotation and retrieval of services in the cloud , 2014, Knowl. Based Syst..
[14] Kim Schouten,et al. Review-level aspect-based sentiment analysis using an ontology , 2018, SAC.
[15] Mark S. Fox,et al. Ontologies for transportation research: A survey , 2018 .
[16] Xiaoduan Sun,et al. Text Mining and Topic Modeling of Compendiums of Papers from Transportation Research Board Annual Meetings , 2016 .
[17] Abdelkarim Erradi,et al. Sentiment Analysis as a Service: A Social Media Based Sentiment Analysis Framework , 2017, 2017 IEEE International Conference on Web Services (ICWS).
[18] Saif Mohammad,et al. Sentiment Analysis of Short Informal Texts , 2014, J. Artif. Intell. Res..
[19] Mingdong Tang,et al. WE-LDA: A Word Embeddings Augmented LDA Model for Web Services Clustering , 2017, 2017 IEEE International Conference on Web Services (ICWS).
[20] Zijian Wang,et al. Semi supervised classification of scientific and technical literature based on semi supervised hierarchical description of improved latent dirichlet allocation (LDA) , 2018, Cluster Computing.
[21] John G. Breslin,et al. INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification , 2016, SemEval@NAACL-HLT.
[22] Jing Zhou,et al. Hate Speech Detection with Comment Embeddings , 2015, WWW.
[23] D. Teja Santosh,et al. Opinion Mining of Online Product Reviews from Traditional LDA Topic Clusters using Feature Ontology Tree and Sentiwordnet , 2016 .
[24] Jeffrey Pennington,et al. GloVe: Global Vectors for Word Representation , 2014, EMNLP.
[25] Yoshua Bengio,et al. A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..
[26] Kyung Sup Kwak,et al. Fuzzy Ontology-Based Sentiment Analysis of Transportation and City Feature Reviews for Safe Traveling , 2017, ArXiv.
[27] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[28] Umberto Straccia,et al. Fuzzy Ontology Representation using OWL 2 , 2010, Int. J. Approx. Reason..
[29] Dipanjan Sarkar,et al. Analyzing Movie Reviews Sentiment , 2018 .
[30] Wei Xu,et al. Secondhand seller reputation in online markets: A text analytics framework , 2018, Decis. Support Syst..
[31] Aitor García Pablos,et al. W2VLDA: Almost unsupervised system for Aspect Based Sentiment Analysis , 2017, Expert Syst. Appl..
[32] Hideyuki Tanaka,et al. Public Sentiment and Demand for Used Cars after A Large-Scale Disaster: Social Media Sentiment Analysis with Facebook Pages , 2018, ArXiv.
[33] Mauro Dragoni,et al. A fuzzy-based strategy for multi-domain sentiment analysis , 2018, Int. J. Approx. Reason..
[34] João Filipe Figueiredo Pereira,et al. Social Media Text Processing and Semantic Analysis for Smart Cities , 2017, ArXiv.
[35] Alaa Mohasseb,et al. Domain specific syntax based approach for text classification in machine learning context , 2017, 2017 International Conference on Machine Learning and Cybernetics (ICMLC).
[36] Taeho Hong,et al. Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach , 2017 .
[37] Namuk Ko,et al. Identifying Product Opportunities Using Social Media Mining: Application of Topic Modeling and Chance Discovery Theory , 2018, IEEE Access.
[38] Kyung Sup Kwak,et al. The IoT: Exciting Possibilities for Bettering Lives: Special application scenarios , 2016, IEEE Consumer Electronics Magazine.
[39] Eric Atwell,et al. Aspect Based Sentiment Analysis Framework using Data from Social Media Network , 2017 .
[40] Tsvi Kuflik,et al. Enhancing transport data collection through social media sources: methods, challenges and opportunities for textual data , 2015 .
[41] Maria Virvou,et al. Comparative Evaluation of Algorithms for Sentiment Analysis over Social Networking Services , 2017, J. Univers. Comput. Sci..
[42] Keeley A. Crockett,et al. Modelling road congestion using ontologies for big data analytics in smart cities , 2017, 2017 International Smart Cities Conference (ISC2).
[43] Janyce Wiebe,et al. Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis , 2005, HLT.
[44] Marwan Bikdash,et al. From social media to public health surveillance: Word embedding based clustering method for twitter classification , 2017, SoutheastCon 2017.
[45] Aytug Onan,et al. LDA-based Topic Modelling in Text Sentiment Classification: An Empirical Analysis , 2016, Int. J. Comput. Linguistics Appl..
[46] Xiaolin Zheng,et al. Review Sentiment Analysis Based on Deep Learning , 2015, 2015 IEEE 12th International Conference on e-Business Engineering.
[47] Long Ma,et al. A Multi-label Text Classification Framework: Using Supervised and Unsupervised Feature Selection Strategy , 2017 .
[48] Min Song,et al. Topic-based content and sentiment analysis of Ebola virus on Twitter and in the news , 2016, J. Inf. Sci..
[49] Hee Yong Youn,et al. A novel classification approach based on Naïve Bayes for Twitter sentiment analysis , 2017, KSII Trans. Internet Inf. Syst..
[50] Zoraida Callejas Carrión,et al. Sentiment Analysis: From Opinion Mining to Human-Agent Interaction , 2016, IEEE Transactions on Affective Computing.
[51] Luiz Antonio Ribeiro,et al. Impurity effects and temperature influence on the exciton dissociation dynamics in conjugated polymers , 2013 .
[52] Hui Zhang,et al. Public Sentiments Analysis Based on Fuzzy Logic for Text , 2016, Int. J. Softw. Eng. Knowl. Eng..
[53] Veronikha Effendy,et al. Sentiment Analysis on Twitter about the Use of City Public Transportation Using Support Vector Machine Method , 2016 .
[54] Munir Ahmad,et al. Analyzing the Performance of SVM for Polarity Detection with Different Datasets , 2017 .
[55] Daeyoung Park,et al. Merged Ontology and SVM-Based Information Extraction and Recommendation System for Social Robots , 2017, IEEE Access.
[56] Marco Guerini,et al. SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis , 2015, IEEE Transactions on Affective Computing.
[57] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[58] Francisco Herrera,et al. Consensus vote models for detecting and filtering neutrality in sentiment analysis , 2018, Inf. Fusion.
[59] Preslav Nakov,et al. SemEval-2016 Task 4: Sentiment Analysis in Twitter , 2016, *SEMEVAL.
[60] Yugyung Lee,et al. Ontology Mapping Framework with Feature Extraction and Semantic Embeddings , 2018, 2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W).
[61] Emma E. Regentova,et al. A Hybrid Model Using Logistic Regression and Wavelet Transformation to Detect Traffic Incidents , 2016 .
[62] Miguel Ángel Rodríguez-García,et al. Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach , 2017, Comput. Math. Methods Medicine.
[63] Yong-Gi Kim,et al. Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system , 2015, Applied Intelligence.
[64] Bing Liu,et al. Mining and summarizing customer reviews , 2004, KDD.
[65] Dong-Hong Ji,et al. A topic-enhanced word embedding for Twitter sentiment classification , 2016, Inf. Sci..
[66] Keet Sugathadasa,et al. Deriving a representative vector for ontology classes with instance word vector embeddings , 2017, 2017 Seventh International Conference on Innovative Computing Technology (INTECH).