1 |
Basiri, M. E., & Kabiri, A. (2017). Sentence-level sentiment analysis in Persian, International Conference on Pattern Recognition and Image Analysis (IPRIA), 84-89. https://doi.org/10.1109/PRIA.2017.7983023.
DOI
|
2 |
Ding, X., Liu, B., & Yu, P. S. (2008). A holistic lexicon-based approach to opinion mining. Proceedings of the 2008 international conference on web search and data mining, Feb. 11-12, Palo Alto, California.
|
3 |
Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the 10th ACM SIGKDD international conference on Knowledge discovery and data mining, Aug. 22-25, Seattle WA, USA.
|
4 |
Marstawi, A., Sharef, N. M., Aris, T. N. M., & Mustapha, A. (2017). Ontology-based aspect extraction for an improved sentiment analysis in summarization of product reviews. Proceedings of the 8th International Conference on Computer Modeling and Simulation, Jan. 20-23, Canberra, Australia.
|
5 |
Pontiki, M., Galanis, D., Papageorgiou, H., Androutsopoulos, I., Manandhar, S., Al-Smadi, M., Al-Ayyoub, M., Zhao, Y., Qin, B., & De Clercq, O. (2016). SemEval-2016 task 5: Aspect based sentiment analysis. Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), Jun. 16-17, San Diego, CA, USA.
|
6 |
Hauser, J. R., & Clausing, D. (1988). The house of quality, Harvard Business Review, 1-13.
|
7 |
Wang, Y., Mo, D. Y., & Tseng, M. M. (2018). Mapping customer needs to design parameters in the front end of product design by applying deep learning. CIRP Annals, 67(1), 145-148.
DOI
|
8 |
Alvarez-Lopez, T., Juncal-Martinez, J., Fernandez-Gavilanes, M., Costa-Montenegro, E., & Gonzalez-Castano, F. J. (2016). SemEval-2016 task 5: SVM and CRF for aspect detection and unsupervised aspect-based sentiment analysis. Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), Jun. 16-17, San Diego, CA, USA.
|
9 |
Chung, S. J. (2020). Aspect-Level Analysis and Predictive Modeling for Electric Vehicle Based on Aspect-Based Sentiment Analysis Using Machine Learning, M.E. Thesis, Graduate School of Seoul National University, Seoul, Korea.
|
10 |
Hasan, A., Moin, S., Karim, A., & Shamshirband, S. (2018). Machine learning-based sentiment analysis for twitter accounts. Mathematical and Computational Applications, 23(1), 11, https://doi.org/10.3390/mca23010011.
DOI
|
11 |
Huang, Z., Xu, W., & Yu, K. (2015). Bidirectional LSTM-CRF models for sequence tagging. arXiv preprint arXiv:1508.01991.
|
12 |
Kim, D. I. (2018). Consumer Sentiment Analysis Using Smartphone Review Comments, M.E. Thesis, Graduate School of engineering, Yonsei University, Seoul, Korea.
|
13 |
Timoshenko, A., & Hauser, J. R. (2019). Identifying customer needs from user-generated content. Marketing Science, 38(1), 1-20.
DOI
|
14 |
Park, H. J., Song, M. C., & Shin, K. S. (2018). Sentiment analysis of korean reviews using cnn: Focusing on morpheme embedding. Journal of intelligence and information systems, 24(2), 59-83.
DOI
|
15 |
Patra, B. G., Soumik, M., Das, D., & Sivaji, B. (2014). Ju_cse: A conditional random field (crf) based approach to aspect based sentiment analysis. Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), Aug. 23-24, Dublin, Ireland.
|
16 |
Sarawgi, K., & Pathak, V. (2017). Opinion mining: aspect level sentiment analysis using SentiWordNet and Amazon web services. International Journal of Computer Applications, 158(6), 31-36.
DOI
|
17 |
Singh, J., Singh, G., & Singh, R. (2016). A review of sentiment analysis techniques for opinionated web text. CSI transactions on ICT, 4(2-4), 241-247.
DOI
|
18 |
Song, M., Park, H., & Shin, K. S. (2019). Attention-based long short-term memory network using sentiment lexicon embedding for aspect-level sentiment analysis in Korean. Information Processing & Management, 56(3), 637-653.
DOI
|
19 |
Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., & Liu, B. (2011). Combining lexicon -based and learning-based methods for Twitter sentiment analysis. HP Laboratories, Technical Report HPL-2011, 89.
|