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[1] https://win.dgbas.gov.tw/fies/a11.asp?year=106(2022.01.02) [2] Pang, B., Lee, L., and Vaithyanathan, S., “Thumbs up?: Sentiment classification using machine learning techniques,” Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, Vol. 10, pp. 79–86, 2002. [3] Liu, B., Sentiment Analysis and Opinion Mining, Morgan & Claypool, California, pp. 58–89, 2012. [4] Pontiki, M., Galanis, D., Pavlopoulos, J., Papageorgiou, H., Androutsopoulos, I., and Manandhar, S., “SemEval-2014 Task 4: Aspect Based Sentiment Analysis,” Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2014), pp. 27-35, 2014. [5] Pontiki, M., Galanis, D., Papageorgiou, H., Manandhar, S., and Androutsopoulos, I., “SemEval-2015 Task 12: Aspect Based Sentiment Analysis,” Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval 2015), pp. 486–495. [6] Liu, B., Sentiment Analysis and subjectivity, Handbook of Natural Language Processing 2, pp. 627-666, 2010. [7] Li, G.-R., and Chang, C.-H., “Semantic role labeling for opinion target extraction from chinese social network,” Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1042–1047, 2019. [8] Schuster, M., and Paliwal, K. K., “Bidirectional recurrent neural networks,” IEEE Transactions on Signal Processing, Vol. 45, No. 11, pp. 2673–2681, 1997. [9] Lafferty, J., McCallum, A., and Pereira, F. C. N., “Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data,” Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282–289, 2001. [10] Luo, H., Li, T., Liu, B., Wang, B., and Unger, H., “Improving Aspect Term Extraction with Bidirectional Dependency Tree Representation,” Proceedings of the 2019 IEEE/ACM Transactions on Audio, Speech, and Language Processing, pp. 1201-1212, 2019. [11] Xiang, Y., He, H., and Zheng, J., “Aspect Term Extraction Based on MFE-CRF,” Information, Vol. 9, No. 8, pp. 198-212, 2018. [12] Zhang, Q., and Shi, C., “Exploiting BERT with Global-Local Context and Label Dependency for Aspect Term Extraction,” 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 354–362, 2020. [13] Xu, H., Liu, B., Shu, L., and Yu, P. S., “Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction,” Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, Vol. 2, pp. 592–598, 2018. [14] Zeng, B., Yang, H., Xu, R., Zhou, W., and Han, X., “LCF: A Local Context Focus Mechanism for Aspect-Based Sentiment Classification,” Applied Sciences, Vol. 9, No. 16, pp. 3389-3410, 2019. [15] Caruana, R., “Multitask Learning,” Machine Learning, Vol. 28, No. 1, pp. 41-75, 1997. [16] Yang, H., Zeng, B., Yang, J., Song, Y., and Xu, R., “A Multi-task Learning Model for Chinese-oriented Aspect Polarity Classification and Aspect Term Extraction,” Neuro computing, Vol. 419, pp. 344–356, 2021. [17] Luo, H., Li, T., Liu, B. and Zhang, J., “DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-Extraction,” Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 591–601, 2019. [18] Li, X., Bing, L., Zhang, W., and Lam, W., “Exploiting BERT for End-to-End Aspect-based Sentiment Analysis,” Proceedings of the 5th Workshop on Noisy User-Generated Text (W-NUT 2019), pp. 34–41, 2019. [19] Saeidi, M., Bouchard, G., Liakata, M., and Riedel, S., “SentiHood: Targeted Aspect Based Sentiment Analysis Dataset for Urban Neighbourhoods,” Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 1546-1556, 2016. [20] Wan, H., Yang, Y., Du, J., Liu, Y., Qi, K., and Pan, J. Z., “Target-Aspect-Sentiment Joint Detection for Aspect-Based Sentiment Analysis,” Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, No. 5, pp. 9122–9129, 2020. [21] Hochreiter, S., and Schmidhuber, J., “Long short-term memory,” Neural computation, Vol. 9, No. 8, pp. 1735–1780, 1997. [22] Pal, S., Ghosh, S., and Nag, A., “Sentiment Analysis in the Light of LSTM Recurrent Neural Networks,” International Journal of Synthetic Emotions, Vol. 9, pp. 33–39, 2018. [23] Senevirathne, L., Demotte, P., Karunanayake, B., Munasinghe, U., and Ranathunga, S., “Sentiment Analysis for Sinhala Language using Deep Learning Techniques,” arXiv e-prints, Vol. 2011, No. 7280, pp. 1-12, 2020. [24] Devlin, J., Chang, M. W., Lee, K. and Toutanova, K., “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, pp. 4171–4186, 2019. [25] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I., “Attention Is All You Need,” arXiv e-prints, Vol. 1706, No. 3762, pp. 1-15, 2017. [26] Shen, Y., and Liu, J., “Comparison of Text Sentiment Analysis based on Bert and Word2vec,” 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer (ICFTIC), pp. 144–147, 2021. [27] Mikolov, T., Chen, K., Corrado, G., and Dean, J., “Efficient Estimation of Word Representations in Vector Space,” arXiv e-prints, Vol. 1301, No. 3781, pp. 1-12, 2013. [28] Lee, C.-C., Gao, Z., and Tsai, C.-L., “BERT-Based Stock Market Sentiment Analysis,” 2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), pp. 1–2, 2020. [29] Tang, D., Qin, B., and Liu, T., “Aspect Level Sentiment Classification with Deep Memory Network,” Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 214–224, 2016. [30] Mitchell, M., Aguilar, J., Wilson, T., and Durme, B. V., “Open Domain Targeted Sentiment,” Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 1643–1654, 2013. [31] Li, X., Bing, L., Li, P., and Lam, W., “A Unified Model for Opinion Target Extraction and Target Sentiment Prediction,” Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33, No. 01, pp. 6714-6721, 2019. [32] Zhang, M., Zhang, Y., and Vo, D. T., “Neural Networks for Open Domain Targeted Sentiment,” Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 612-621, 2015. [33] Ryan, M., Scraping With Python: Collecting More Data from the Modern Web, O’Reilly Media, Preface, 2018. [34] https://pypi.org/project/requests/(2021.12.10) [35] https://www.crummy.com/software/BeautifulSoup/bs4/doc/(2021.12.10) [36] Sang, E. F. T. K., and Veenstra, J., “Representing text chunks,” Proceedings of the Ninth Conference on European Chapter of the Association for Computational Linguistics, pp. 173-179, 1999. [37] Zhang, H., Gan, W., and Jiang, B., “Machine Learning and Lexicon Based Methods for Sentiment Classification: A Survey,” 2014 11th Web Information System and Application Conference, pp. 262-265, 2014. [38] Sharma, A., and Dey, S., “A comparative study of feature selection and machine learning techniques for Sentiment analysis,” Proceedings of the 2012 ACM Research in Applied Computation Symposium, pp. 1-7, 2012. [39] https://github.com/ldkrsi/jieba-zh_TW(2021.11.1) [40] Augustyniak, Ł., Kajdanowicz, T., and Kazienko, P., “Aspect Detection using Word and Char Embeddings with (Bi)LSTM and CRF,” 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp. 43–50, 2019. [41] Chen, T., Xu, R., He, Y., and Wang, X., “Improving sentiment analysis via sentence type classification using BiLSTM-CRF and CNN,” Expert Systems with Applications, Vol. 72, pp. 221–230, 2017.
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