References
- B. Liu, "Sentiment analysis and opinion mining," Synthesis lectures on human language technologies, vol. 5, no. 1, pp. 1-167, 2012. https://doi.org/10.2200/S00416ED1V01Y201204HLT016
- M. Giatsoglou, M. G. Vozalis, K. Diamantaras, A. Vakali, G. Sarigiannidis, and K. C. Chatzisavvas, "Sentiment analysis leveraging emotions and word embeddings," Expert Systems with Applications, vol. 69, pp. 214-224, 2017. https://doi.org/10.1016/j.eswa.2016.10.043
- P. Zhang and Z. He, "Using data-driven feature enrichment of text representation and ensemble technique for sentence-level polarity classification," Journal of Information Science, vol. 41, no. 4, pp. 531-549, 2015. https://doi.org/10.1177/0165551515585264
- Z. Zhai, B. Liu, H. Xu, and P. Jia, "Clustering product features for opinion mining," in Proceedings of the fourth ACM international conference on Web search and data mining, 2011, pp. 347-354.
- N. F. F. Da Silva, E. R. Hruschka, and E. R. Hruschka, "Tweet sentiment analysis with classifier ensembles," Decision Support Systems, vol. 66, pp. 170-179, 2014. https://doi.org/10.1016/j.dss.2014.07.003
- A. Tripathy, A. Agrawal, and S. K. Rath, "Classification of sentiment reviews using n-gram machine learning approach," Expert Systems with Applications, vol. 57, pp. 117-126, 2016. https://doi.org/10.1016/j.eswa.2016.03.028
- S. Wang and C. D. Manning, "Baselines and bigrams: Simple, good sentiment and topic classification," in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Vol. 2, 2012, pp. 90-94.
- Q. Le and T. Mikolov, "Distributed representations of sentences and documents," in Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014, pp. 1188-1196.
- A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, "Learning Word Vectors for Sentiment Analysis," Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142-150, 2011.
- T. Mikolov, K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representations in Vector Space," In Proceedings of workshop at ICLR, pp. 1-12, 2013.
- B. Pang, L. Lee, and S. Vaithyanathan, "Thumbs up?: sentiment classification using machine learning techniques," in Proceedings of the ACL-02 conference on Empirical methods in natural language processing, Vol. 10, 2002, pp. 79-86.
- D. Chatzakou and A. Vakali, "Harvesting opinions and emotions from social media textual resources," IEEE Internet Computing, vol. 19, no. 4, pp. 46-50, 2015. https://doi.org/10.1109/MIC.2015.28
- R. Xia, C. Zong, and S. Li, "Ensemble of feature sets and classification algorithms for sentiment classification," Information Sciences, vol. 181, no. 6, pp. 1138-1152, 2011. https://doi.org/10.1016/j.ins.2010.11.023
- G. Wang, J. Sun, J. Ma, K. Xu, and J. Gu, "Sentiment classification: The contribution of ensemble learning," Decision Support Systems, vol. 57, no. 1, pp. 77-93, 2014. https://doi.org/10.1016/j.dss.2013.08.002
- H.-S. L. Dong-yub Lee Jae-Choon Jo, "User Sentiment Analysis on Amazon Fashion Product Review Using Word Embedding," Journal of the Korea Convergence Society, vol. 8, no. 4, pp. 1-8, 2017. https://doi.org/10.15207/JKCS.2017.8.4.001
- J. Lilleberg, Y. Zhu, and Y. Zhang, "Support Vector Machines and Word2vec for Text Classification with Semantic Features," Cognitive Informatics & Cognitive Computing (ICCI* CC), 2015 IEEE 14th International Conference on. IEEE, 2015., pp. 136-140, 2015.
- Y. Ren, R. Wang, and D. Ji, "A topic-enhanced word embedding for Twitter sentiment classification," Information Sciences, vol. 369, pp. 188-198, 2016. https://doi.org/10.1016/j.ins.2016.06.040
- Y. Bengio, H. Schwenk, J.-S. Senecal, F. Morin, and J.-L. Gauvain, "Neural Probabilistic Language Models," in Innovations in Machine Learning: Theory and Applications, D. E. Holmes and L. C. Jain, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 137-186.
- K.-M. Ahn, Y.-S. Kim, Y.-H. Kim, and Y.-H. Seo, "Sentiment Classification of Movie Reviews using Levenshtein Distance," Journal of Digital Contents Society, vol. 14, no. 4, pp. 581-587, Dec. 2013. https://doi.org/10.9728/dcs.2013.14.4.581
- Y. Kim and M. Song, "A Study on Analyzing Sentiments on Movie Reviews by Multi-Level Sentiment Classifier," Journal of Intelligence and Information Systems, vol. 22, no. 3, pp. 71-89, 2016. https://doi.org/10.13088/jiis.2016.22.3.071
- Y. Jung, K. Park, T. Lee, J. Chae, and S. Jung, "A corpus-based approach to classifying emotions using Korean linguistic features," Cluster Computing, vol. 20, no. 1, pp. 583-595, 2017. https://doi.org/10.1007/s10586-017-0777-8
- C. Lee, K. Hyun, Y. Byeong, M. Mun, and S. Joo, "Informal Quality Data Analysis via Sentimental analysis and," Journal of the Korean Society for Quality Management, vol. 45, no. 1, pp. 117-127, 2017. https://doi.org/10.7469/JKSQM.2017.45.1.117
- Y. Kim and H. Shin, "Finding Sentiment Dimension in Vector Space of Movie Reviews: An Unsupervised Approach," Journal of Cognitive Science, pp. 85-101, 2017.
- S.-Y. O. Chan Heo, "A Novel Method for Constructing Sentiment Dictionaries using Word2vec and Label Propagation," Journal of Korean Institute of Next Generation Computing, vol. 13, no. 2, pp. 93-101, 2017.
- E. L. Park and S. Cho, "KoNLPy: Korean natural language processing in Python," in Proceedings of the 26th Annual Conference on Human & Cognitive Language Technology, 2014, pp. 133-136.
Cited by
- Word2Vec을 활용한 제품군별 시장규모 추정 방법에 관한 연구 vol.26, pp.1, 2020, https://doi.org/10.13088/jiis.2020.26.1.001