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    政大機構典藏 > 商學院 > 金融學系 > 學位論文 >  Item 140.119/135940
    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135940


    Title: 利用領域適應建構自然語言情緒分類模型:以台灣財經新聞為例
    A Semantic Sentiment Classification Model based on Domain Adaption: the Case of TWSE News
    Authors: 張群
    Chang, Chun
    Contributors: 江彌修
    Chiang, Mi-Hsiu
    張群
    Chang, Chun
    Keywords: 自然語言處理
    預訓練
    下游任務
    情緒分類
    領域適應
    主題分組
    Nature Language Process
    Pre-training
    Downstream-task
    Domain Adaption
    Topic Grouping
    Date: 2021
    Issue Date: 2021-07-01 18:01:18 (UTC+8)
    Abstract: 本研究在台灣財經新聞上,使用領域適應以及主題分組的方式解決 BERT 在無監督文本與領域文本,以及下游訓練文本之間都存在的文本異質性,並且 依照上述方式方邊分別建立情緒分類模型。在領域適應對於情緒分類效果不論 在損失函數、混淆矩陣,以及 ROC 曲線與 AUC 值上的結論都與 Araci (2019)相 同,並無發現顯著的差異。然而在主題分組上對於情緒分類效果上,在上述的 指標中都能發現顯著差異;除此之外,若觀察主題分組之後的模型,亦能進一 步發現負面新聞的理解能力提升,解決負面新聞在先天樣本不足下的劣勢。
    In this paper, we implement domain adaption and topic grouping to deal with heterogeneity of corpus between pre-training and domain corpus, and among downstream corpus for TWSE news semantic sentiment classification model. The empirical results show that domain adaption model can’t conclude a significant effect, which agrees with Araci (2019). However, the topic grouping model can achieve a better performance than other models, and increase the understanding of negative corpus.
    Reference: [1] Araci, D. (2019), “FinBERT: Financial Sentiment Analysis with Pre-trained.
    Language Models.”, “arXiv preprint arXiv:1908.10063”
    [2] Bengio, Y., Ducharme, R., Vincent, R. and Jauvin C. (2003), “A Neural Probabilistic Language Model”, “Journal of Machine Learning Research 3 (2003)”, 1137–1155
    [3] Devlin, J., Chang, M. W., Lee, K. and Toutanova, K. (2018), “BERT: Pre- training of. Deep Bidirectional Transformers for Language Understanding.”, “arXiv preprint arXiv 1811.03600v2”
    [4] Santos, C. D., and Gatti, M. (2014). “Deep Convolutional Neural Networks for Sentiment. Analysis of Short Texts, in Proceedings of COLING.”, “The 25th International Conference on Computational Linguistics.”, 69–78
    [5] Howard, J. and Ruder, S. (2018). Universal Language Model Fine- tuning for. Text Classification. (jan 2018). arXiv preprint arXiv: 1801.06146
    [6] Jotheeswaran, J. and Koteeswaran, S. (2015), “Decision Tree Based Feature Selection. and Multilayer Perceptron for Sentiment Analysis.”, “ARPN J Eng Appl Sci, ISSN 1819–6608 10(14)”, 5883– 5894
    [7] Mikolov, T., Chen, K., Corrado, G. and Dean J. (2013), “Efficient Estimation of Word. Representations in Vector Space”, “arXiv preprint arXiv 1301.3781”
    [8] Pranckevičius, T. and Marcinkevicius V. (2017), “Comparison of naive bayes, random. forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification.”, “Baltic J Mod Comput 5:221”
    [9] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L. and Gomez , A. N., Kaiser, L., Polosukhin I. (2017), “Attention Is All You Need”, “arXiv preprint arXiv:1706.03762”
    [10] Wang, X., Liu, Y., Sun, C., Wang, B., Wang, X. (2015), “Predicting. Polarities of Tweets by Composing Word Embeddings with Long Short-Term Memory”, “in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), Beijing, China”, 1343–1353
    [11] Yang, Y., Cer, D., Ahmad, A., Guo, M., Law, J., Constant, N., Abrego, A. G., Yuan, S., Tar, C., Sung, Y., Strope, B., Kurzweil, R. (2019), “Multilingual Universal Sentence Encoder for Semantic Retrieval”, “arXiv preprint arXiv:1907.04307”
    [12] Wang, Y., Wang, M., Fujita, H. (2020), “Word sense disambiguation: A. comprehensive knowledge exploitation framework”, “Knowledge- Based Systems 190 (Feb, 2020): 105030”
    [13] Zhang, D. W., Xu, H., Su, Z., Xu, Y. (2015), “Chinese comments. sentiment classification based on word2vec and SVMperf.”, “Expert Systems with Applications, 42(4)”, 1857–1863.
    Description: 碩士
    國立政治大學
    金融學系
    108352022
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0108352022
    Data Type: thesis
    DOI: 10.6814/NCCU202100606
    Appears in Collections:[金融學系] 學位論文

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