|
1.Arora, S., Ge, R., Halpern, Y., Mimno, D., Moitra, A., Sontag, D., Wu, Y., & Zhu, M. (2013). A practical algorithm for topic modeling with provable guarantees. International Conference on Machine Learning, 280–288. 2.Banerjee, A., Dhillon, I. S., Ghosh, J., & Sra, S. (2005). Clustering on the unit hypersphere using von Mises-Fisher distributions. Journal of Machine Learning Research, 6(Sep), 1345–1382. 3.Baroni, M., Dinu, G., & Kruszewski, G. (2014). Don’t count, predict! A systematic comparison of context-counting vs. Context-predicting semantic vectors. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 238–247. 4.Batmanghelich, K., Saeedi, A., Narasimhan, K., & Gershman, S. (2016). Nonparametric spherical topic modeling with word embeddings. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, Volume 2: Short Papers, 537. 5.Bengio, Y., Ducharme, R., Vincent, P., & Jauvin, C. (2003). A neural probabilistic language model. Journal of Machine Learning Research, 3(Feb), 1137–1155. 6.Bianchi, F., Terragni, S., Hovy, D., Nozza, D., & Fersini, E. (2021). Cross-lingual Contextualized Topic Models with Zero-shot Learning. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 1676–1683. https://www.aclweb.org/anthology/2021.eacl-main.143 7.Bischof, J., & Airoldi, E. M. (2012). Summarizing topical content with word frequency and exclusivity. Proceedings of the 29th International Conference on Machine Learning (ICML-12), 201–208. 8.Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational Inference: A Review for Statisticians. Journal of the American Statistical Association, 112(518), 859–877. https://doi.org/10.1080/01621459.2017.1285773 9.Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3(Jan), 993–1022. 10.Bojanowski, P., Grave, E., Joulin, A., & Mikolov, T. (2017). Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5, 135–146. 11.Bond, F., & Foster, R. (2013). Linking and extending an open multilingual wordnet. ACL, 1352–1362. 12.Boyd-Graber, J., & Blei, D. M. (2009). Multilingual topic models for unaligned text. Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, 75–82. 13.Chang, C.-H., & Hwang, S.-Y. (2021). A word embedding-based approach to cross-lingual topic modeling. Knowledge and Information Systems. https://doi.org/10.1007/s10115-021-01555-7 14.Chang, C.-H., Hwang, S.-Y., & Wu, M.-L. (2021). Learning bilingual sentiment lexicon for online reviews. Electronic Commerce Research and Applications, 47, 101037. https://doi.org/10.1016/j.elerap.2021.101037 15.Chang, C.-H., Hwang, S.-Y., & Xui, T.-H. (2018). Incorporating Word Embedding into Cross-Lingual Topic Modeling. 2018 IEEE International Congress on Big Data (BigData Congress), 17–24. https://doi.org/10.1109/BigDataCongress.2018.00010 16.Chang, C.-H., Wu, M.-L., & Hwang, S.-Y. (2019). An Approach to Cross-Lingual Sentiment Lexicon Construction. 2019 IEEE International Congress on Big Data (BigDataCongress), 129–131. https://doi.org/10.1109/BigDataCongress.2019.00030 17.Cheng, M., & Jin, X. (2019). What do Airbnb users care about? An analysis of online review comments. Int. J. Hosp. Manage., 76, 58–70. 18.Das, R., Zaheer, M., & Dyer, C. (2015). Gaussian LDA for Topic Models with Word Embeddings. 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), 795–804. https://doi.org/10.3115/v1/P15-1077 19.Deng, S., Sinha, A. P., & Zhao, H. (2017). Adapting sentiment lexicons to domain-specific social media texts. Decision Support Systems, 94, 65–76. 20.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, NAACL-HLT 2019, Volume 1 (Long and Short Papers), 4171–4186. 21.Dieng, A. B., Ruiz, F. J. R., & Blei, D. M. (2020). Topic Modeling in Embedding Spaces. Transactions of the Association for Computational Linguistics, 8, 439–453. https://doi.org/10.1162/tacl_a_00325 22.Dietterich, T. G. (1998). Approximate statistical tests for comparing supervised classification learning algorithms. Neural Computation, 10(7), 1895–1923. 23.Dinu, G., & Baroni, M. (2013). Dissect-distributional semantics composition toolkit. Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 31–36. 24.Fan, Z.-P., Che, Y.-J., & Chen, Z.-Y. (2017). Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, 90–100. 25.Fang, X., & Zhan, J. (2015). Sentiment analysis using product review data. Journal of Big Data, 2(1), 5. 26.Faruqui, M., & Dyer, C. (2014). Improving vector space word representations using multilingual correlation. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, 462–471. 27.Fast, E., Chen, B., & Bernstein, M. S. (2016). Empath: Understanding topic signals in large-scale text. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 4647–4657. 28.Fujinuma, Y., Boyd-Graber, J., & Paul, M. J. (2019). A resource-free evaluation metric for cross-lingual word embeddings based on graph modularity. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 4952–4962. 29.Gao, D., Wei, F., Li, W., Liu, X., & Zhou, M. (2015). Cross-lingual sentiment lexicon learning with bilingual word graph label propagation. Computational Linguistics, 41(1), 21–40. 30.Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101(suppl 1), 5228–5235. 31.Hamilton, W. L., Clark, K., Leskovec, J., & Jurafsky, D. (2016). Inducing domain-specific sentiment lexicons from unlabeled corpora. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2016, 595. 32.Hao, S., Boyd-Graber, J. L., & Paul, M. J. (2018). Lessons from the bible on modern topics: Adapting topic model evaluation to multilingual and low-resource settings. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, 1–6. 33.Hao, S., & Paul, M. J. (2020). An Empirical Study on Crosslingual Transfer in Probabilistic Topic Models. Computational Linguistics, 46(1), 95–134. https://doi.org/10.1162/coli_a_00369 34.Harris, Z. S. (1954). Distributional structure. Word & World, 10(2–3), 146–162. 35.Hassan, A., Abu-Jbara, A., Jha, R., & Radev, D. (2011). Identifying the semantic orientation of foreign words. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers-Volume 2, 592–597. 36.Heyman, G., Vulić, I., & Moens, M.-F. (2016). C-BiLDA extracting cross-lingual topics from non-parallel texts by distinguishing shared from unshared content. Data Mining and Knowledge Discovery, 30(5), 1299–1323. https://doi.org/10.1007/s10618-015-0442-x 37.Hogenboom, A., Heerschop, B., Frasincar, F., Kaymak, U., & de Jong, F. (2014). Multi-lingual support for lexicon-based sentiment analysis guided by semantics. Decision Support Systems, 62, 43–53. 38.Hu, M., & Liu, B. (2004). Mining and summarizing customer reviews. Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 168–177. 39.Hu, Y., Zhai, K., Eidelman, V., & Boyd-Graber, J. (2014). Polylingual Tree-Based Topic Models for Translation Domain Adaptation. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1166–1176. https://doi.org/10.3115/v1/P14-1110 40.Huang, C.-L., Chung, C. K., Hui, N., Lin, Y.-C., Seih, Y.-T., Lam, B. C., Chen, W.-C., Bond, M. H., & Pennebaker, J. W. (2012). The development of the Chinese linguistic inquiry and word count dictionary. Chinese Journal of Psychology. 41.Jagarlamudi, J., & Daumé, H. (2010). Extracting Multilingual Topics from Unaligned Comparable Corpora. In C. Gurrin, Y. He, G. Kazai, U. Kruschwitz, S. Little, T. Roelleke, S. Rüger, & K. van Rijsbergen (Eds.), Advances in Information Retrieval (Vol. 5993, pp. 444–456). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-12275-0_39 42.Jiang, D., Tong, Y., & Song, Y. (2016). Cross-lingual topic discovery from multilingual search engine query log. ACM Transactions on Information Systems (TOIS), 35(2), 9. 43.Khoo, C. S. G., & Johnkhan, S. B. (2018). Lexicon-based sentiment analysis: Comparative evaluation of six sentiment lexicons. Journal of Information Science, 44(4), 491–511. 44.Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. ArXiv:1312.6114 [Cs, Stat]. http://arxiv.org/abs/1312.6114 45.Lample, G., Conneau, A., Ranzato, M., Denoyer, L., & Jégou, H. (2018). Word translation without parallel data. ICLR. 46.Lau, J. H., Newman, D., & Baldwin, T. (2014). Machine reading tea leaves: Automatically evaluating topic coherence and topic model quality. Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, 530–539. 47.Lazaridou, A., Dinu, G., & Baroni, M. (2015). Hubness and pollution: Delving into Cross-Space mapping for Zero-Shot learning. 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), 270–280. 48.Lewis, D. D., Yang, Y., Rose, T. G., & Li, F. (n.d.). RCV1: A New Benchmark Collection for Text Categorization Research. Journal of Machine Learning Research, 5, 361–397. 49.Liu, Y., Bi, J.-W., & Fan, Z.-P. (2017). Ranking products through online reviews: A method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf. Fusion, 36, 149–161. 50.Lopez, R., Boyeau, P., Yosef, N., Jordan, M. I., & Regier, J. (2020). Decision-Making with Auto-Encoding Variational Bayes. ArXiv:2002.07217 [Cs, Stat]. http://arxiv.org/abs/2002.07217 51.Lucas, J., Tucker, G., Grosse, R., & Norouzi, M. (2019). Don’t Blame the ELBO! A Linear VAE Perspective on Posterior Collapse. ArXiv:1911.02469 [Cs, Stat]. http://arxiv.org/abs/1911.02469 52.Ma, T., & Nasukawa, T. (2016). Inverted bilingual topic models for lexicon extraction from non-parallel data. ArXiv Preprint ArXiv:1612.07215. 53.MacKay, D. J. C. (1998). Choice of Basis for Laplace Approximation. Machine Learning, 33(1), 77–86. https://doi.org/10.1023/A:1007558615313 54.Manaman, H. S., Jamali, S., & AleAhmad, A. (2016). Online reputation measurement of companies based on user-generated content in online social networks. Computers in Human Behavior, 54, 94–100. 55.McAuley, J. J., & Leskovec, J. (2013). From amateurs to connoisseurs: Modeling the evolution of user expertise through online reviews. WWW, 897–908. 56.Mihalcea, R., Banea, C., & Wiebe, J. (2007). Learning multilingual subjective language via cross-lingual projections. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, 976–983. 57.Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. ArXiv Preprint ArXiv:1301.3781. 58.Mikolov, T., Le, Q. V., & Sutskever, I. (2013). Exploiting similarities among languages for machine translation. ArXiv Preprint ArXiv:1309.4168. 59.Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 3111–3119. 60.Miller, G. A. (1998). WordNet: An electronic lexical database. MIT press. 61.Mimno, D., Wallach, H. M., Naradowsky, J., Smith, D. A., & McCallum, A. (2009). Polylingual topic models. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP ’09, 2, 880. https://doi.org/10.3115/1699571.1699627 62.Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436–465. 63.Mrksic, N., Séaghdha, D. Ó., Thomson, B., Gasic, M., Rojas-Barahona, L. M., Su, P.-H., Vandyke, D., Wen, T.-H., & Young, S. J. (2016). Counter-fitting word vectors to linguistic constraints. NAACL-HLT, 142–148. 64.Mrksic, N., Vulic, I., Séaghdha, D. Ó., Leviant, I., Reichart, R., Gasic, M., Korhonen, A., & Young, S. J. (2017). Semantic specialization of distributional word vector spaces using monolingual and cross-lingual constraints. TACL, 5, 309–324. 65.Nagamma, P., Pruthvi, H. R., Nisha, K. K., & Shwetha, N. H. (2015). An improved sentiment analysis of online movie reviews based on clustering for box-office prediction. International Conference on Computing, Communication Automation, 933–937. 66.Nguyen, D. Q., Billingsley, R., Du, L., & Johnson, M. (2015). Improving Topic Models with Latent Feature Word Representations. Transactions of the Association for Computational Linguistics, 3, 299–313. https://doi.org/10.1162/tacl_a_00140 67.Ni, X., Sun, J.-T., Hu, J., & Chen, Z. (2009). Mining multilingual topics from wikipedia. Proceedings of the 18th International Conference on World Wide Web, 1155–1156. 68.Oliveira, N., Cortez, P., & Areal, N. (2016). Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 85, 62–73. 69.Ono, M., Miwa, M., & Sasaki, Y. (2015). Word embedding-based antonym detection using thesauri and distributional information. NAACL-HLT, 984–989. 70.Patra, B., Moniz, J. R. A., Garg, S., Gormley, M. R., & Neubig, G. (2019). Bilingual lexicon induction with semi-supervision in non-isometric embedding spaces. ACL, 184–193. 71.Pennington, J., Socher, R., & Manning, C. D. (2014). Glove: Global vectors for word representation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1532–1543. 72.Pires, T., Schlinger, E., & Garrette, D. (2019). How multilingual is multilingual BERT? ArXiv Preprint ArXiv:1906.01502. 73.Qi, Y., Sachan, D. S., Felix, M., Padmanabhan, S., & Neubig, G. (2018). When and why are pre-trained word embeddings useful for neural machine translation? NAACL-HLT, 529–535. 74.Qiu, G., Liu, B., Bu, J., & Chen, C. (2009). Expanding domain sentiment lexicon through double propagation. Twenty-First International Joint Conference on Artificial Intelligence, 1199–1204. 75.Rambocas, M., & Pacheco, B. G. (2018). Online sentiment analysis in marketing research: A review. Journal of Research in Interactive Marketing. 76.Reisinger, J., Waters, A., Silverthorn, B., & Mooney, R. J. (2010). Spherical topic models. Proceedings of the 27th International Conference on Machine Learning (ICML-10), 903–910. 77.Rezende, D. J., Mohamed, S., & Wierstra, D. (2014). Stochastic Backpropagation and Approximate Inference in Deep Generative Models. Proceedings of the 31st International Conference on Machine Learning, 32, 1278–1286. 78.Ruder, S., Vulić, I., & Søgaard, A. (2019). A survey of cross-lingual word embedding models. Journal of Artificial Intelligence Research, 65, 569–631. 79.Schwenk, H., & Li, X. (2018, May 7). A Corpus for Multilingual Document Classification in Eight Languages. Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018). LREC 2018. 80.Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces, 63–70. 81.Smith, S. L., Turban, D. H., Hamblin, S., & Hammerla, N. Y. (2017). Offline bilingual word vectors, orthogonal transformations and the inverted softmax. ArXiv Preprint ArXiv:1702.03859. 82.Sra, S. (2012). A short note on parameter approximation for von Mises-Fisher distributions: And a fast implementation of I s (x). Computational Statistics, 27(1), 177–190. 83.Srivastava, A., & Sutton, C. (2017). Autoencoding Variational Inference For Topic Models. ArXiv:1703.01488 [Stat]. http://arxiv.org/abs/1703.01488 84.Stajner, T., & Mladenic, D. (2019). Cross-lingual document similarity estimation and dictionary generation with comparable corpora. Knowledge and Information Systems, 58(3), 729–743. 85.Steinberger, J., Ebrahim, M., Ehrmann, M., Hurriyetoglu, A., Kabadjov, M., Lenkova, P., Steinberger, R., Tanev, H., Vázquez, S., & Zavarella, V. (2012). Creating sentiment dictionaries via triangulation. Decision Support Systems, 53(4), 689–694. 86.Taboada, M., Brooke, J., Tofiloski, M., Voll, K. D., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307. 87.Tamura, A., & Sumita, E. (2016). Bilingual segmented topic model. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1266–1276. 88.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. 89.Tian, L., Wong, D. F., Chao, L. S., Quaresma, P., Oliveira, F., & Yi, L. (2014). UM-Corpus: A large english-chinese parallel corpus for statistical machine translation. LREC, 1837–1842. 90.Vulić, I., De Smet, W., & Moens, M.-F. (2013). Cross-language information retrieval models based on latent topic models trained with document-aligned comparable corpora. Information Retrieval, 16(3), 331–368. 91.Vulić, I., Glavaš, G., Mrkšić, N., & Korhonen, A. (2018). Post-specialisation: Retrofitting vectors of words unseen in lexical resources. NAACL-HLT, 516–527. 92.Wu, F., Huang, Y., Song, Y., & Liu, S. (2016). Towards building a high-quality microblog-specific Chinese sentiment lexicon. Decision Support Systems, 87, 39–49. 93.Xing, C., Wang, D., Liu, C., & Lin, Y. (2015). Normalized word embedding and orthogonal transform for bilingual word translation. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 1006–1011. 94.Xing, F. Z., Pallucchini, F., & Cambria, E. (2019). Cognitive-inspired domain adaptation of sentiment lexicons. Inf. Process. Manag., 56(3), 554–564. 95.Yang, W., Boyd-Graber, J., & Resnik, P. (2019). A Multilingual Topic Model for Learning Weighted Topic Links Across Corpora with Low Comparability. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 1243–1248. https://doi.org/10.18653/v1/D19-1120 96.Yang, Y., Cer, D., Ahmad, A., Guo, M., Law, J., Constant, N., Abrego, G. H., Yuan, S., Tar, C., Sung, Y.-H., Strope, B., & Kurzweil, R. (2019). Multilingual Universal Sentence Encoder for Semantic Retrieval. ArXiv:1907.04307 [Cs]. http://arxiv.org/abs/1907.04307 97.Yuan, M., Van Durme, B., & Ying, J. L. (2018). Multilingual Anchoring: Interactive Topic Modeling and Alignment Across Languages. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, & R. Garnett (Eds.), Advances in Neural Information Processing Systems (Vol. 31). Curran Associates, Inc. https://proceedings.neurips.cc/paper/2018/file/28b9f8aa9f07db88404721af4a5b6c11-Paper.pdf 98.Zhong, S., & Ghosh, J. (2005). Generative model-based document clustering: A comparative study. Knowledge and Information Systems, 8(3), 374–384. 99.Zhou, G., Zhu, Z., He, T., & Hu, X. T. (2016). Cross-lingual sentiment classification with stacked autoencoders. Knowledge and Information Systems, 47(1), 27–44.
|