| Reference: | 李姵儒（2018）：《國中生情緒主題寫作文本之情緒詞彙特徵與心理健康相關研究》（碩士論文，國立臺灣師範大學），國立臺灣師範大學圖書館機構典藏。https://doi.org/10.6345/THE.NTNU.DEPC.012.2018.F02
卓淑玲、陳學志、鄭昭明（2013）。台灣地區華人情緒與相關心理生理資料庫─中文情緒詞常模研究，中華心理學刊，55(4)，493–523。http://dx.doi.org/10.6129/CJP.20131026
胡中凡、陳彥丞、卓淑玲、陳學志、張雨霖、宋曜廷（2017）。1200個中文雙字詞的聯想常模與其被聯想反應參照表。教育心理學報，49(1)，137-161。https://doi.org/10.6251/BEP.20161111
黃金蘭、Chung, C. K.、Hui, N.、林以正、謝亦泰、程威詮、Lam, B.、Bond. M., Pennebaker, J. W.（2012）。中文版「語文探索與字詞計算」詞典之建立。中華心理學刊，54(2)，185–201。http://dx.doi.org/10.6129/CJP.2012.5402.04
楊立行、許清芳（2019）。社群媒體上分手文章的性別差異：文本分析取徑。中華心理學刊，61(3)，209–230。https://doi.org/10.6129/CJP.201909_61(3).0003
Baroni-Urbani, C., & Buser, M. W. (1976). Similarity of Binary Data. Systematic Zoology, 25(3), 251–259. JSTOR. https://doi.org/10.2307/2412493
Barrett, L. F. (2014). The Conceptual Act Theory: A Précis. Emotion Review, 6(4), 292–297. https://doi.org/10.1177/1754073914534479
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. J. Mach. Learn. Res., 3, 993–1022.
Boyd, R. L., & Schwartz, H. A. (2021). Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field. Journal of Language and Social Psychology, 40(1), 21–41. https://doi.org/10.1177/0261927X20967028
Chang, C.-Y., Chen, Y.-C., Tsai, M.-N., Sung, Y.-T., Chang, Y.-L., Lin, S.-Y., Cho, S.-L., Chang, T.-H., & Chen, H.-C. (2022). The Corpus of Emotional Valences for 33,669 Chinese Words Based on Big Data. HCI in Business, Government and Organizations: 9th International Conference, HCIBGO 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, June 26 – July 1, 2022, Proceedings, 141–152. https://doi.org/10.1007/978-3-031-05544-7_11
Cohen, A. S., Minor, K. S., Najolia, G. M., & Hong, S. L. (2009). A laboratory-based procedure for measuring emotional expression from natural speech. Behavior Research Methods, 41(1), 204–212. https://doi.org/10.3758/BRM.41.1.204
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). 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, Volume 1 (Long and Short Papers), 4171–4186. https://doi.org/10.18653/v1/N19-1423
Eichstaedt, J. C., Kern, M. L., Yaden, D. B., Schwartz, H. A., Giorgi, S., Park, G., Hagan, C. A., Tobolsky, V. A., Smith, L. K., Buffone, A., Iwry, J., Seligman, M. E. P., & Ungar, L. H. (2021). Closed- and open-vocabulary approaches to text analysis: A review, quantitative comparison, and recommendations. Psychological Methods, 26(4), 398–427. https://doi.org/10.1037/met0000349
Gernsbacher, M. A., Goldsmith, H. H., & Robertson, R. R. W. (1992). Do Readers Mentally Represent Characters’ Emotional States? Cognition and Emotion, 6(2), 89–111. https://doi.org/10.1080/02699939208411061
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(1), 5228. https://doi.org/10.1073/pnas.0307752101
Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. (2007). Topics in semantic representation. Psychological Review, 114(2), 211–244. https://doi.org/10.1037/0033-295X.114.2.211
Grün, B., & Hornik, K. (2011). topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software, 40(13), 1–30. https://doi.org/10.18637/jss.v040.i13
Gygax, P., Garnham, A., & Oakhill, J. (2004). Inferring characters’ emotional states: Can readers infer specific emotions? Language and Cognitive Processes, 19(5), 613–639. https://doi.org/10.1080/01690960444000016
Gygax, P., Oakhill, J., & Garnham, A. (2003). The representation of characters’ emotional responses: Do readers infer specific emotions? Cognition and Emotion, 17(3), 413–428. https://doi.org/10.1080/02699930244000048
Huang, C.-R., Lee, L.-H., Qu, W., Hong, J.-F., & Yu, S. (2008). Quality Assurance of Automatic Annotation of Very Large Corpora: A Study based on heterogeneous Tagging System. Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08). Presented at the Marrakech, Morocco. Marrakech, Morocco: European Language Resources Association (ELRA). Retrieved from http://www.lrec-conf.org/proceedings/lrec2008/pdf/686_paper.pdf
Iliev, R., Dehghani, M., & Sagi, E. (2015). Automated text analysis in psychology: Methods, applications, and future developments. Language and Cognition: An Interdisciplinary Journal of Language and Cognitive Science, 7(2), 265–290. https://doi.org/10.1017/langcog.2014.30
Jaccard, P. (1912). The Distribution of the Flora in the Alpine Zone. The New Phytologist, 11(2), 37–50.
Kahn, J. H., Tobin, R. M., Massey, A. E., & Anderson, J. A. (2007). Measuring Emotional Expression with the Linguistic Inquiry and Word Count. The American Journal of Psychology, 120(2), 263–286. https://doi.org/10.2307/20445398
Kross, E., Verduyn, P., Boyer, M., Drake, B., Gainsburg, I., Vickers, B., Ybarra, O., & Jonides, J. (2019). Does counting emotion words on online social networks provide a window into people’s subjective experience of emotion? A case study on Facebook. Emotion, 19(1), 97–107. https://doi.org/10.1037/emo0000416
Ku, L.-W., & Chen, H.-H. (2007). Mining opinions from the Web: Beyond relevance retrieval. Journal of the American Society for Information Science and Technology, 58(12), 1838–1850. https://doi.org/10.1002/asi.20630
Lake, B. M., & Murphy, G. L. (2021). Word meaning in minds and machines. Psychological Review, Advance online publication. https://doi.org/10.1037/rev0000297
Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato’s problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological Review, 104(2), 211–240. https://doi.org/10.1037/0033-295X.104.2.211
Lenci, A. (2018). Distributional Models of Word Meaning. Annual Review of Linguistics, 4(1), 151–171. https://doi.org/10.1146/annurev-linguistics-030514-125254
Lee, L.-H., Li, J.-H., & Yu, L.-C. (2022). Chinese EmoBank: Building Valence-Arousal Resources for Dimensional Sentiment Analysis. ACM Trans. Asian Low-Resour. Lang. Inf. Process., 21(4). https://doi.org/10.1145/3489141
Li, P.-H., Fu, T.-J., & Ma, W.-Y. (2020). Why Attention? Analyze BiLSTM Deficiency and Its Remedies in the Case of NER. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8236-8244. https://doi.org/10.1609/aaai.v34i05.6338
Lin, S.-Y., Chen, H.-C., Chang, T.-H., Lee, W.-E., & Sung, Y.-T. (2019). CLAD: A corpus-derived Chinese Lexical Association Database. Behavior Research Methods, 51(5), 2310–2336. https://doi.org/10.3758/s13428-019-01208-2
Lindquist, K. A., MacCormack, J. K., & Shablack, H. (2015). The role of language in emotion: Predictions from psychological constructionism. Frontiers in Psychology, 6, Article 444. https://doi.org/10.3389/fpsyg.2015.00444
McDonnell, M., Owen, J. E., & Bantum, E. O. (2020). Identification of Emotional Expression With Cancer Survivors: Validation of Linguistic Inquiry and Word Count. JMIR Form Res, 4(10), e18246. https://doi.org/10.2196/18246
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed Representations of Words and Phrases and Their Compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems, 2, 3111–3119.
Mumper, M. L., & Gerrig, R. J. (2021). The Representation of Emotion Inferences. Discourse Processes, 58(8), 681–702. https://doi.org/10.1080/0163853X.2021.1882196
Ng, B. C., Cui, C., & Cavallaro, F. (2019). The annotated lexicon of chinese emotion words. WORD, 65(2), 73–92. https://doi.org/10.1080/00437956.2019.1599543
Pennebaker, J. W., & Beall, S. K. (1986). Confronting a traumatic event: Toward an understanding of inhibition and disease. Journal of Abnormal Psychology, 95(3), 274–281. https://doi.org/10.1037/0021-843X.95.3.274
Pennebaker, J. W., Booth, R. J., & Francis, M. E. (2007). Linguistic Inquiry and Word Count: LIWC [Computer software]. Austin, TX: LIWC.net.
Pennebaker, J. W., Mehl, M. R., & Niederhoffer, K. G. (2003). Psychological Aspects of Natural Language Use: Our Words, Our Selves. Annual Review of Psychology, 54(1), 547–577. https://doi.org/10.1146/annurev.psych.54.101601.145041
Poria, S., Hazarika, D., Majumder, N., & Mihalcea, R. (2020). Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research. IEEE Transactions on Affective Computing. Advanced online publication. https://doi.org/10.1109/TAFFC.2020.3038167
Reimers, N., & Gurevych, I. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, 3980–3990. https://doi.org/10.18653/v1/D19-1410
Robertson, S. (2004). Understanding inverse document frequency: On theoretical arguments for IDF. Journal of Documentation, 60(5), 503–520. https://doi.org/10.1108/00220410410560582
Rude, S. S., Gortner, E.-M., & Pennebaker, J. W. (2004). Language use of depressed and depression-vulnerable college students. Cognition and Emotion, 18(8), 1121–1133. https://doi.org/10.1080/02699930441000030
Settanni, M., & Marengo, D. (2015). Sharing feelings online: Studying emotional well-being via automated text analysis of Facebook posts. Frontiers in Psychology, 6, 1045. https://doi.org/10.3389/fpsyg.2015.01045
Sun, J., Schwartz, H. A., Son, Y., Kern, M. L., & Vazire, S. (2020). The language of well-being: Tracking fluctuations in emotion experience through everyday speech. Journal of Personality and Social Psychology, 118(2), 364–387. https://doi.org/10.1037/pspp0000244
Tan, P.-N., Kumar, V., & Srivastava, J. (2004). Selecting the right objective measure for association analysis. Knowledge Discovery and Data Mining (KDD 2002), 29(4), 293–313. https://doi.org/10.1016/S0306-4379(03)00072-3
Tausczik, Y. R., & Pennebaker, J. W. (2010). The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In U. von Luxburg, I. Guyon, & S. Bengio (Eds.), Proceedings of the 31st international conference on neural information processing systems (pp. 6000-6010). Curran Associates. https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
Wang, W., Hernandez, I., Newman, D. A., He, J., & Bian, J. (2016). Twitter Analysis: Studying US Weekly Trends in Work Stress and Emotion. Applied Psychology, 65(2), 355–378. https://doi.org/10.1111/apps.12065
Yu, L.-C., Lee, L.-H., & Wong, K.-F. (2016). Overview of the IALP 2016 shared task on Dimensional Sentiment Analysis for Chinese Words. 2016 International Conference on Asian Language Processing (IALP), 156–160. https://doi.org/10.1109/IALP.2016.7875957
Yu, L.-C., Lee, L.-H., Hao, S., Wang, J., He, Y., Hu, J., Lai, K. R., & Zhang, X. (2016). Building Chinese Affective Resources in Valence-Arousal Dimensions. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 540–545. https://doi.org/10.18653/v1/N16-1066
Yu, L. C., Lee, L. H., Wang, J., & Wong, K. F. (2017). IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases. Proceedings of the IJCNLP 2017, Shared Tasks, 9–16. https://aclanthology.org/I17-4002
Zhang, T., Kishore, V., Wu, F., Weinberger, K. Q., & Artzi, Y. (2020). BERTScore: Evaluating Text Generation with BERT. International Conference on Learning Representations. https://openreview.net/forum?id=SkeHuCVFDr
Zhao, N., Jiao, D., Bai, S., & Zhu, T. (2016). Evaluating the Validity of Simplified Chinese Version of LIWC in Detecting Psychological Expressions in Short Texts on Social Network Services. PLOS ONE, 11(6), e0157947. https://doi.org/10.1371/journal.pone.0157947 |
