Chuanjun Zhao
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Abstract
Traditional methods for sentiment analysis, when applied in a monolingual context, often yield less than optimal results in multilingual settings. This underscores the need for a more thorough exploration of cross-lingual sentiment analysis (CLSA) methodologies to improve analytical effectiveness. CLSA, confronted with obstacles such as linguistic disparities and a lack of resources, seeks to evaluate sentiments across a range of languages. First, the research background, challenges, existing solution ideas, and evaluation tasks of CLSA are summarized. Subsequently, new perspectives including different granularity levels, machine translation support, and sentiment transfer strategies perspectives are highlighted. Finally, potential avenues for future research are discussed.
- [1] . 2017. Cross-lingual sentiment analysis without (good) translation. In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP, 2017 Volume 1: Long Papers). Asian Federation of Natural Language Processing, 506–515. Retrieved from https://www.aclweb.org/anthology/I17-1051Google Scholar
- [2] . 2014. Cross-lingual Twitter polarity detection via projection across word-aligned corpora. In Proceedings of the International Conference on Machine Learning Conference and Workshop on Issues of Sentiment Discovery and Opinion Mining(ICML WISDOM 2014). 1–12. Retrieved from https://www.sentic.net/wisdom2014mansour.pdfGoogle Scholar
- [3] . 2020. Borrow from rich cousin: Transfer learning for emotion detection using cross lingual embedding. Expert Systems with Applications 139, C (
Jan 2020), 112851.DOI: Google ScholarDigital Library
- [4] . 2018. Solving data sparsity for aspect based sentiment analysis using cross-linguality and multi-linguality. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT Volume 1: Long Papers). Association for Computational Linguistics, 572–582.
DOI: Google ScholarCross Ref
- [5] . 2020. Persian sentiment analysis without training data using cross-lingual word embeddings. In Proceedings of the 2020 10th International Symposium on Telecommunications (IST). IEEE, 78–82.
DOI: Google ScholarCross Ref - [6] . 2019. UAMSA: Unified approach for multilingual sentiment analysis using GATE. In Proceedings of the 6th Conference on the Engineering of Computer Based Systems (ECBS’19). Association for Computing Machinery, 1–5.
DOI: Google ScholarDigital Library - [7] . 2020. Leveraging multilingual resources for language invariant sentiment analysis. In Proceedings of the 22nd Annual Conference of the European Association for Machine Translation (EAMT). European Association for Machine Translation, 71–79. Retrieved from https://aclanthology.org/2020.eamt-1.9Google Scholar
- [8] . 2016. An evaluation of machine translation for multilingual sentence-level sentiment analysis. In Proceedings of the 31st Annual ACM Symposium on Applied Computing (SAC’16). Association for Computing Machinery, 1140–1145.
DOI: Google ScholarDigital Library - [9] . 2011. Multilingual sentiment analysis using latent semantic indexing and machine learning. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining Workshops (ICDMW’11). IEEE, 45–52.
DOI: Google ScholarDigital Library - [10] . 2011. Sentiment analysis with a multilingual pipeline. In Proceedings of the 12th International Conference on Web Information Systems Engineering(Volume 6997 WISE 2011). Springer, 129–142.
DOI: Google ScholarCross Ref - [11] . 2012. Multilingual sentiment analysis using machine translation? In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis (WASSA’12). Association for Computational Linguistics, 52–60. Retrieved from https://aclanthology.org/W12-3709Google ScholarDigital Library
- [12] . 2014. Comparative experiments using supervised learning and machine translation for multilingual sentiment analysis. Computer Speech and Language 28, 1 (
Jan 2014), 56–75.DOI: Google ScholarDigital Library - [13] . 2012. Cross-lingual sentiment analysis for Indian languages using linked wordnets. In Proceedings of the 24th International Conference on Computational Linguistics 2012(COLING 2012): Posters. 73–82. Retrieved from https://aclanthology.org/C12-2008.pdfGoogle Scholar
- [14] . 2011. Multilingual sentiment and subjectivity analysis. Multilingual Natural Language Processing 6 (
May 2011), 1–19. Retrieved from https://web.eecs.umich.edu/mihalcea/papers/banea.chap11Google Scholar - [15] . 2019. Embedding projection for targeted cross-lingual sentiment: Model comparisons and a real-world study. Journal of Artificial Intelligence Research 66, 2019 (
Nov 2019), 691–742.DOI: Google ScholarCross Ref - [16] . 2018. Bilingual sentiment embeddings: Joint projection of sentiment across languages. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2483–2493.
DOI: Google ScholarCross Ref - [17] . 2016. Exploring distributional representations and machine translation for aspect-based cross-lingual sentiment classification. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers(COLING 2016). The COLING 2016 Organizing Committee, 1613–1623. Retrieved from https://aclanthology.org/C16-1152Google Scholar
- [18] . 2017. An efficient deep neural architecture for multilingual sentiment analysis in Twitter. In Proceedings of the 30th International Florida Artificial Intelligence Research Society Conference. Association for the Advancement of Artificial Intelligence, 246–251.Google Scholar
- [19] . 2006. Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP’06). Association for Computational Linguistics, 120–128.
DOI: Google ScholarCross Ref - [20] . 2010. Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (EMNLP’10). Association for Computational Linguistics, 45–55. Retrieved from http://umiacs.umd.edu/jbg//docs/jbg-mlslda-2010Google Scholar
- [21] . 2022. A survey on aspect-based sentiment classification. Computing Surveys 55, 4 (
Nov 2022), 1–37.DOI: Google ScholarDigital Library - [22] . 2009. Cross-linguistic sentiment analysis: From English to Spanish. In Proceedings of the International Conference Recent Advances in Natural Language Processing(RANLP-2009). Association for Computational Linguistics, 50–54. Retrieved from https://aclanthology.org/R09-1010Google Scholar
- [23] . 2020. Learning cross-lingual word embeddings from Twitter via distant supervision. In Proceedings of the 14th International AAAI Conference on Web and Social Media(ICWSM 2020 Volume 14). Association for the Advancement of Artificial Intelligence, 72–82.
DOI: Google ScholarCross Ref - [24] . 2016. Affective computing and sentiment analysis. IEEE Intelligent Systems 31, 02 (
Apr 2016), 102–107.DOI: Google ScholarDigital Library - [25] . 2020. SenticNet 6: Ensemble application of symbolic and subsymbolic AI for sentiment analysis. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (CIKM’20). Association for Computing Machinery, 105–114.
DOI: Google ScholarDigital Library - [26] . 2020. Reproducible extraction of cross-lingual topics (rectr). Communication Methods and Measures 14, 4 (
Sep 2020), 285–305.DOI: Google ScholarCross Ref - [27] . 2019. An approach to cross-lingual sentiment lexicon construction. In Proceedings of the 2019 IEEE International Congress on Big Data (BigDataCongress). IEEE, 129–131.
DOI: Google ScholarCross Ref - [28] . 2017. Modeling language discrepancy for cross-lingual sentiment analysis. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM’17). Association for Computing Machinery, 117–126.
DOI: Google ScholarDigital Library - [29] . 2015. Learning to adapt credible knowledge in cross-lingual sentiment analysis. 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). Association for Computational Linguistics, 419–429. Retrieved from https://aclanthology.org/P15-1041.pdfGoogle ScholarCross Ref
- [30] . 2017. Cross-lingual sentiment relation capturing for cross-lingual sentiment analysis. In Proceedings of the 39th European Conference on Information Retrieval, ECIR 2017. Springer, 54–67.
DOI: Google ScholarCross Ref - [31] . 2018. Adversarial deep averaging networks for cross-lingual sentiment classification. Transactions of the Association for Computational Linguistics 6, 2018 (
Dec 2018), 557–570.DOI: Google ScholarCross Ref - [32] . 2019. Emoji-powered representation learning for cross-lingual sentiment classification. In Proceedings of the 2019 World Wide Web Conference (WWW’19). Association for Computing Machinery, 251–262.
DOI: Google ScholarDigital Library - [33] . 2020. Unsupervised cross-lingual representation learning at scale. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 8440–8451.Google ScholarCross Ref
- [34] . 2019. Cross-Lingual Language Model Pretraining. Curran Associates Inc., Vancouver, Canada, 7059–7969. Retrieved from Google ScholarDigital Library
- [35] . 2016. Multilingual sentiment analysis: State of the art and independent comparison of techniques. Cognitive Computation 8, 4 (
Jun 2016), 757–771.DOI: Google ScholarCross Ref - [36] . 2013. Cross-lingual polarity detection with machine translation. In Proceedings of the 2nd International Workshop on Issues of Sentiment Discovery and Opinion Mining (WISDOM’13). Association for Computing Machinery, 1–8.
DOI: Google ScholarDigital Library - [37] . 2008. Using SentiWordNet for multilingual sentiment analysis. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop. IEEE, 507–512.
DOI: Google ScholarCross Ref - [38] . 2018. Cross-lingual propagation for deep sentiment analysis. In Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI 2018(
AAAI’18/IAAI’18/EAAI’18 , Vol. 32). AAAI press, 5771–5778.DOI: Google ScholarCross Ref - [39] . 2018. Improving cross-lingual word embeddings by meeting in the middle. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 294–304.
DOI: Google ScholarCross Ref - [40] . 2011. Is machine translation ripe for cross-lingual sentiment classification?. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: : Short Papers - Volume 2 (HLT’11). Association for Computational Linguistics, 429–433.
DOI: Google ScholarDigital Library - [41] . 2014. Feature based sentiment analysis of tweets in multiple languages. In Proceedings of the 2014 International Conference on Web Information Systems Engineering(WISE 2014). Springer, 109–124.
DOI: Google ScholarCross Ref - [42] . 2020. Cross-lingual sentiment quantification. IEEE Intelligent Systems 35, 3 (
Jul 2020), 106–114.DOI: Google ScholarDigital Library - [43] . 2014. Cross-lingual short-text document classification for Facebook comments. In Proceedings of the 2014 International Conference on Future Internet of Things and Cloud (FICLOUD’14). IEEE Computer Society, 573–578.
DOI: Google ScholarDigital Library - [44] . 2019. Cross-Lingual and Low-Resource Sentiment Analysis. Ph. D. Dissertation. Columbia University.Google Scholar
- [45] . 2014. Improving vector space word representations using multilingual correlation. In Proceedings of the 2014 International Conference on the European Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 462–471. Retrieved from https://aclanthology.org/E14-1049Google ScholarCross Ref
- [46] . 2020. Cross-lingual unsupervised sentiment classification with multi-view transfer learning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 5759–5771.
DOI: Google ScholarCross Ref - [47] . 2019. Learning bilingual sentiment-specific word embeddings without cross-lingual supervision. In 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). Association for Computational Linguistics, 420–429.
DOI: Google ScholarCross Ref - [48] . 2019. Towards a unified end-to-end approach for fully unsupervised cross-lingual sentiment analysis. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). Association for Computational Linguistics, 1035–1044.
DOI: Google ScholarCross Ref - [49] . 2016. Distributional correspondence indexing for cross-lingual and cross-domain sentiment classification. Journal of Artificial Intelligence Research 55, 2016 (
Jul 2016), 131–163.Google ScholarDigital Library - [50] . 2020. ABSent: Cross-lingual sentence representation mapping with bidirectional GANs. In Proceedings of the AAAI Conference on Artificial Intelligence(Volume 34). Association for the Advancement of Artificial Intelligence, 7756–7763.
DOI: Google ScholarCross Ref - [51] . 2015. Cross-lingual sentiment lexicon learning with bilingual word graph label propagation. Computational Linguistics 41, 1 (
Mar 2015), 21–40.DOI: Google ScholarDigital Library - [52] . 2012. Experiments in cross-lingual sentiment analysis in discussion forums. In Proceedings of the 2012 International Conference on Social Informatics. Springer, 138–151.
DOI: Google ScholarDigital Library - [53] . 2016. Like it or not: A survey of Twitter sentiment analysis methods. ACM Computing Surveys (CSUR) 49, 2 (
Jun 2016), 1–41.DOI: Google ScholarDigital Library - [54] . 2020. Cross-lingual propagation of sentiment information based on bilingual vector space alignment. In Proceedings of the EDBT/ICDT 2020 Joint Conference. 8–10. Retrieved from https://ceur-ws.org/Vol-2578/DARLIAP1.pdfGoogle Scholar
- [55] . 2019. How to (properly) evaluate cross-lingual word embeddings: On strong baselines, comparative analyses, and some misconceptions. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 710–721.
DOI: Google ScholarCross Ref - [56] . 2005. Cross language text categorization by acquiring multilingual domain models from comparable corpora. In Proceedings of the ACL Workshop on Building and Using Parallel Texts (ParaText’05). Association for Computational Linguistics, 9–16.
DOI: Google ScholarCross Ref - [57] . 2006. Exploiting comparable corpora and bilingual dictionaries for cross-language text categorization. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics (ACL-44). Association for Computational Linguistics, 553–560.
DOI: Google ScholarDigital Library - [58] . 2015. Deep feelings: A massive cross-lingual study on the relation between emotions and virality. In Proceedings of the 24th International Conference on World Wide Web (WWW’15 Companion). Association for Computing Machinery, 299–305.
DOI: Google ScholarDigital Library - [59] . 2015. Improving transfer learning in cross lingual opinion analysis through negative transfer detection. In Proceedings of the 8th International Conference on Knowledge Science, Engineering and Management (KSEM 2015). Springer-Verlag, 394–406.
DOI: Google ScholarDigital Library - [60] . 2014. Cross-lingual opinion analysis via negative transfer detection. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Association for Computational Linguistics (ACL), 860–865. Retrieved from https://aclanthology.org/P14-2139Google ScholarCross Ref
- [61] . 2013. A mixed model for cross lingual opinion analysis. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 93–104.
DOI: Google ScholarCross Ref - [62] . 2010. OpinionIt: A text mining system for cross-lingual opinion analysis. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM’10). Association for Computing Machinery, 1199–1208.
DOI: Google ScholarDigital Library - [63] . 2016. A distributed representation-based framework for cross-lingual transfer parsing. Journal of Artificial Intelligence Research 55, 2016 (
Apr 2016), 995–1023.DOI: Google ScholarCross Ref - [64] . 2021. Task-specific pre-training and cross lingual transfer for sentiment analysis in Dravidian code-switched languages. In Proceedings of the 1st Workshop on Speech and Language Technologies for Dravidian Languages. Association for Computational Linguistics, 73–79. Retrieved from https://aclanthology.org/2021.dravidianlangtech-1.9Google Scholar
- [65] . 2014. Bi-view semi-supervised active learning for cross-lingual sentiment classification. Information Processing and Management 50, 5 (
Sep 2014), 718–732.DOI: Google ScholarDigital Library - [66] . 2014. Cross-lingual sentiment classification using multiple source languages in multi-view semi-supervised learning. Engineering Applications of Artificial Intelligence 36, C (
Nov 2014), 195–203.DOI: Google ScholarDigital Library - [67] . 2014. Density based active self-training for cross-lingual sentiment classification. In Proceedings of the Advances in Computer Science and its Applications: CSA 2013(Volume 279). Springer, 1053–1059.
DOI: Google ScholarCross Ref - [68] . 2015. Graph-based semi-supervised learning for cross-lingual sentiment classification. In Proceedings of the Asian Conference on Intelligent Information and Database Systems. Springer, 97–106.
DOI: Google ScholarCross Ref - [69] . 2015. Semi-supervised learning on cross-lingual sentiment analysis with space transfer. In Proceedings of the 2015 IEEE 1st International Conference on Big Data Computing Service and Applications (BIGDATASERVICE’15). IEEE, 371–377.
DOI: Google ScholarDigital Library - [70] . 2011. Latent sentiment model for weakly-supervised cross-lingual sentiment classification. In Proceedings of the 33rd European Conference on Information Retrieval, ECIR 2011 (ECIR 2011). Springer, 214–225.
DOI: Google ScholarDigital Library - [71] . 2019. A survey on classification techniques for opinion mining and sentiment analysis. Artificial Intelligence Review 52, 3 (
Dec 2019), 1495–1545.DOI: Google ScholarDigital Library - [72] . 2013. Towards cross-language sentiment analysis through universal star ratings. In Proceedings of the 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Springer, 69–79.
DOI: Google ScholarCross Ref - [73] . 2014. Multi-lingual support for lexicon-based sentiment analysis guided by semantics. Decision Support Systems 62, 2014 (
Jun 2014), 43–53.DOI: Google ScholarCross Ref - [74] . 2017. Cross-lingual sentiment analysis for Indian regional languages. In Proceedings of the 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT). IEEE, 1–6.
DOI: Google ScholarCross Ref - [75] . 2015. Cross lingual sentiment analysis using modified BRAE. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 159–168. Retrieved from https://aclanthology.org/D15-1016.pdfGoogle ScholarCross Ref
- [76] . 2019. Zero-shot cross-lingual opinion target extraction. In 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). Association for Computational Linguistics, 2486–2495.
DOI: Google ScholarCross Ref - [77] . 2018. Cross-Lingual Sentiment Preservation and Transfer Learning in Binary and Multi-class Classification. Ph. D. Dissertation. Helsingin yliopisto.Google Scholar
- [78] . 2016. Analysis of multi-lingual emotion recognition using auditory attention features. In Proceedings of the 17th Annual Conference of the International Speech Communication Association. International Speech Communication Association, 3613–3617.
DOI: Google ScholarCross Ref - [79] . 2020. Cross-lingual deep neural transfer learning in sentiment analysis. Procedia Computer Science 176, 2020 (
Oct 2020), 128–137.DOI: Google ScholarCross Ref - [80] . 2015. Instance selection improves cross-lingual model training for fine-grained sentiment analysis. In Proceedings of the 19th Conference on Computational Natural Language Learning. Association for Computational Linguistics, 153–163. Retrieved from https://aclanthology.org/K15-1016Google ScholarCross Ref
- [81] . 2020. Stance detection: A survey. ACM Computing Surveys (CSUR) 53, 1 (
Feb 2020), 1–37.DOI: Google ScholarDigital Library - [82] . 2023. Zero-shot learning based cross-lingual sentiment analysis for Sanskrit text with insufficient labeled data. Applied Intelligence 53, 9 (2023), 10096–10113.Google ScholarDigital Library
- [83] . 2017. Microblogging reviews based cross-lingual sentimental classification for cold-start product recommendation. In Proceedings of the 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 1–4.
DOI: Google ScholarCross Ref - [84] . 2019. ArbEngVec: Arabic-English cross-lingual word embedding model. In Proceedings of the 4th Arabic Natural Language Processing Workshop. Association for Computational Linguistics, 40–48.
DOI: Google ScholarCross Ref - [85] . 2015. Aspect-level cross-lingual sentiment classification with constrained SMT. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 781–787. Retrieved from https://aclanthology.org/P15-2128Google ScholarCross Ref
- [86] . 2018. Cross lingual speech emotion recognition: Urdu vs. western languages. In Proceedings of the 2018 International Conference on Frontiers of Information Technology (FIT). IEEE, 88–93.
DOI: Google ScholarCross Ref - [87] . 2017. Structural correspondence learning for cross-lingual sentiment classification with one-to-many mappings. In Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17). AAAI Press, 3490–3496.
DOI: Google ScholarCross Ref - [88] . 2013. Active learning for cross-lingual sentiment classification. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 236–246.
DOI: Google ScholarCross Ref - [89] . 2013. Data quality controlling for cross-lingual sentiment classification. In Proceedings of the 2013 International Conference on Asian Language Processing. IEEE Computer Society, 125–128.
DOI: Google ScholarDigital Library - [90] . 2023. DiCLET-TTS: Diffusion model based cross-lingual emotion transfer for text-to-speechA study between English and Mandarin. IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 2023 (2023), 3418–3430.
DOI: Google ScholarDigital Library - [91] . 2006. Using KCCA for Japanese–English cross-language information retrieval and document classification. Journal of Intelligent Information Systems 27, 2 (
Mar 2006), 117–133.DOI: Google ScholarDigital Library - [92] . 2023. CL-XABSA: Contrastive learning for cross-lingual aspect-based sentiment analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 2023 (2023), 2935–2946.
DOI: Google ScholarDigital Library - [93] . 2014. A cross-lingual joint aspect/sentiment model for sentiment analysis. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management. Association for Computing Machinery, 1089–1098.
DOI: Google ScholarDigital Library - [94] . 2015. An unsupervised cross-lingual topic model framework for sentiment classification. IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, 3 (
Mar 2015), 432–444.DOI: Google ScholarDigital Library - [95] . 2012. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5, 1 (
Jun 2012), 1–167.Google ScholarCross Ref - [96] . 2018. Cross-lingual sentiment classification based on auto-associative memory and convolutional neural networks. Journal of Chinese Information Processing 32, 12 (
Nov 2018), 118–124.DOI: Google ScholarCross Ref - [97] . 2015. Bilingual word representations with monolingual quality in mind. In Proceedings of the 2015 International Conference on North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 151–159. Retrieved from https://aclanthology.org/W15-1521Google ScholarCross Ref
- [98] . 2020. Unsupervised bilingual sentiment word embeddings for cross-lingual sentiment classification. In Proceedings of the 2020 the 4th International Conference on Innovation in Artificial Intelligence. Association for Computing Machinery, 180–183.
DOI: Google ScholarDigital Library - [99] . 2020. XLP at SemEval-2020 Task 9: Cross-lingual models with focal loss for sentiment analysis of code-mixing language. In Proceedings of the 14th Workshop on Semantic Evaluation. International Committee for Computational Linguistics, 975–980.
DOI: Google ScholarCross Ref - [100] . 2011. Using bilingual parallel corpora for cross-lingual textual entailment. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 1336–1345. Retrieved from Google ScholarDigital Library
- [101] . 2012. Cross-lingual mixture model for sentiment classification. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (ACL’12). Association for Computational Linguistics, 572–581. Retrieved from https://aclanthology.org/P12-1060Google ScholarDigital Library
- [102] . 2016. Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders. In Proceedings of the 1st Workshop on Representation Learning for NLP. Association for Computational Linguistics, 121–126.
DOI: Google ScholarCross Ref - [103] . 2007. Learning multilingual subjective language via cross-lingual projections. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics. Association for Computational Linguistics, 976–983. Retrieved from https://aclanthology.org/P07-1123.pdfGoogle Scholar
- [104] . 2017. Adapting sentiment analysis system from English to Slovak. In Proceedings of the 2017 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 1–8.
DOI: Google ScholarCross Ref - [105] . 2021. Deep learning–based text classification: A comprehensive review. ACM Computing Surveys (CSUR) 54, 3 (
Apr 2021), 1–40.DOI: Google ScholarDigital Library - [106] . 2016. How translation alters sentiment. Journal of Artificial Intelligence Research 55, 2016 (
Jan 2016), 95–130.DOI: Google ScholarCross Ref - [107] . 2001. Cross-lingual information retrieval based on LSI with multiple word spaces. In Proceedings of the 2nd NTCIR Workshop Meeting on Evaluation of Chinese and Japanese Text Retrieval and Text Summarization. Citeseer, 1–8. Retrieved from https://aclanthology.org/www.mt-archive.info/jnl/NTCIR-2001-MoriGoogle Scholar
- [108] . 2018. A cross-lingual approach for building multilingual sentiment lexicons. In Proceedings of the International Conference on Text, Speech, and Dialogue. Springer, 259–266.
DOI: Google ScholarCross Ref - [109] . 2009. Mining cross-lingual/cross-cultural differences in concerns and opinions in blogs. In Proceedings of the International Conference on Computer Processing of Oriental Languages (ICCPOL’09). Springer, 213–224.
DOI: Google ScholarDigital Library - [110] . 2017. English and Malay cross-lingual sentiment lexicon acquisition and analysis. In Proceedings of the International Conference on Information Science and Applications: ICISA 2017. Springer, 467–475.
DOI: Google ScholarCross Ref - [111] . 2018. Cross-lingual and multilingual speech emotion recognition on English and French. In Proceedings of the 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5769–5773.
DOI: Google ScholarDigital Library - [112] . 2021. Cross lingual speech emotion recognition via triple attentive asymmetric convolutional neural network. International Journal of Intelligent Systems 36, 1 (
Jan 2021), 53–71.DOI: Google ScholarDigital Library - [113] . 2005. Cross-lingual bootstrapping of semantic lexicons: the case of FrameNet. In Proceedings of the 20th National Conference on Artificial Intelligence (AAAI’05). AAAI Press, 1087–1092. Retrieved from Google ScholarCross Ref
- [114] . 2011. Cross-lingual sentiment classification via bi-view non-negative matrix tri-factorization. In Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2011. Springer, 289–300.
DOI: Google ScholarCross Ref - [115] . 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering 22, 10 (
Oct 2010), 1345–1359.DOI: Google ScholarDigital Library - [116] . 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2, 1 (
May 2008), 1–135.DOI: Google ScholarDigital Library - [117] . 2020. SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets. In Proceedings of the 14th International Workshop on Semantic Evaluation (SemEval-2020). International Committee for Computational Linguistics.
DOI: Google ScholarCross Ref - [118] . 2021. Cross-lingual learning for text processing: A survey. Expert Systems with Applications 165, 2021 (
Mar 2021), 113765.DOI: Google ScholarCross Ref - [119] . 2011. Cross-lingual adaptation using structural correspondence learning. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 1 (
Oct 2011), 1–22.DOI: Google ScholarDigital Library - [120] . 2018. Cross-lingual lexical sememe prediction. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 358–368.
DOI: Google ScholarCross Ref - [121] . 2017. A Karaka based approach to cross lingual sentiment analysis. International Journal of Languages, Literature and Linguistics 3, 4 (
Dec 2017), 226–229.DOI: Google ScholarCross Ref - [122] . 2020. Cross-lingual emotion lexicon induction using representation alignment in low-resource settings. In Proceedings of the 28th International Conference on Computational Linguistics. International Committee on Computational Linguistics, 5879–5890.
DOI: Google ScholarCross Ref - [123] . 2018. Cross-lingual sentiment transfer with limited resources. Machine Translation 32, 1 (
Nov 2018), 143–165.DOI: Google ScholarDigital Library - [124] . 2018. Emotion detection in cross-lingual text based on bidirectional LSTM. In Proceedings of the 2nd International Conference on Security with Intelligent Computing and Big-data Services, (SICBS-2018). Springer, 838–845.
DOI: Google ScholarCross Ref - [125] . 2020. Revisiting the context window for cross-lingual word embeddings. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 995–1005.
DOI: Google ScholarCross Ref - [126] . 2019. A survey of cross-lingual word embedding models. Journal of Artificial Intelligence Research 65, 2019 (
Aug 2019), 569–631.DOI: Google ScholarDigital Library - [127] . 2016. Cross lingual speech emotion recognition using canonical correlation analysis on principal component subspace. In Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 5800–5804.
DOI: Google ScholarDigital Library - [128] . 2013. An unsupervised data-driven cross-lingual method for building high precision sentiment lexicons. In Proceedings of the 2013 IEEE 7th International Conference on Semantic Computing (ICSC’13). IEEE Computer Society, 184–190.
DOI: Google ScholarDigital Library - [129] . 2020. Development of sentiment lexicon in Bengali utilizing corpus and cross-lingual resources. In Proceedings of the 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 237–244.
DOI: Google ScholarCross Ref - [130] . 2020. Polarity detection in a cross-lingual sentiment analysis using spaCy. In Proceedings of the 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO). IEEE, 490–496.
DOI: Google ScholarCross Ref - [131] . 2020. Cross-lingual sentiment analysis via AAE and BiGRU. In Proceedings of the 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). IEEE, 237–241.
DOI: Google ScholarCross Ref - [132] . 2015. Learning cross-lingual word embeddings via matrix co-factorization. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Association for Computational Linguistics, 567–572. Retrieved from https://aclanthology.org/P15-2093.pdfGoogle ScholarCross Ref
- [133] . 2020. LT3 at SemEval-2020 task 9: Cross-lingual embeddings for sentiment analysis of Hinglish social media text. In Proceedings of the 14th Workshop on Semantic Evaluation (SemEval 2020). International Committee for Computational Linguistics, 1288–1293.
DOI: Google ScholarCross Ref - [134] . 2016. Borrow a little from your rich cousin: Using embeddings and polarities of english words for multilingual sentiment classification. In Proceedings of the 26th International Conference on Computational Linguistics: Technical Papers. The COLING 2016 Organizing Committee, 3053–3062. Retrieved from https://aclanthology.org/C16-1287Google Scholar
- [135] . 2011. Multilingual entity-centered sentiment analysis evaluated by parallel corpora. In Proceedings of the International Conference Recent Advances in Natural Language Processing 2011. Association for Computational Linguistics, 770–775. Retrieved from https://aclanthology.org/R11-1113.pdfGoogle Scholar
- [136] . 2002. Cross-lingual document similarity calculation using the multilingual thesaurus EUROVOC. In Proceedings of the International Conference on Intelligent Text Processing and Computational Linguistics (CICLing’02). Springer, 415–424. Retrieved from Google ScholarDigital Library
- [137] . 2022. Hope speech detection for Dravidian languages using cross-lingual embeddings with stacked encoder architecture. SN Computer Science 3, 1 (
Nov 2022), 1–15.DOI: Google ScholarDigital Library - [138] . 2014. Learning bilingual embedding model for cross-language sentiment classification. In Proceedings of the 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT). IEEE, 134–141.
DOI: Google ScholarDigital Library - [139] . 2016. Cross-lingual wikification using multilingual embeddings. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, 589–598. Retrieved from https://aclanthology.org/N16-1072Google ScholarCross Ref
- [140] . 2014. Metaphor detection with cross-lingual model transfer. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 248–258. Retrieved from https://aclanthology.org/P14-1024Google ScholarCross Ref
- [141] , et al. 2022. A survey on sentiment analysis techniques for Twitter. In Data Mining Approaches for Big Data and Sentiment Analysis in Social Media. IGI Global, 57–90.
DOI: Google ScholarCross Ref - [142] . 2017. Supervised sentiment analysis in multilingual environments. Information Processing and Management 53, 3 (
May 2017), 595–607.DOI: Google ScholarDigital Library - [143] . 2008. Using bilingual knowledge and ensemble techniques for unsupervised Chinese sentiment analysis. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (EMNLP’08). Association for Computational Linguistics, 553–561. Retrieved from https://aclanthology.org/D08-1058.pdfGoogle ScholarCross Ref
- [144] . 2008. Using only cross-document relationships for both generic and topic-focused multi-document summarizations. Information Retrieval 11, 1 (
Nov 2008), 25–49.DOI: Google ScholarDigital Library - [145] . 2009. Co-training for cross-lingual sentiment classification. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP (ACL’09). Association for Computational Linguistics, 235–243. Retrieved from Google ScholarDigital Library
- [146] . 2011. Bilingual co-training for sentiment classification of Chinese product reviews. Computational Linguistics 37, 3 (
Jan 2011), 587–616.DOI: Google ScholarDigital Library - [147] . 2012. A comparative study of cross-lingual sentiment classification. In Proceedings of the 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology- Volume 01 (WI-IAT’12). IEEE Computer Society, 24–31.
DOI: Google ScholarCross Ref - [148] . 2010. Cross-language document summarization based on machine translation quality prediction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL’10). Association for Computational Linguistics, 917–926. Retrieved from Google ScholarDigital Library
- [149] . 2020. Coarse alignment of topic and sentiment: A unified model for cross-lingual sentiment classification. IEEE Transactions on Neural Networks and Learning Systems 32, 2 (
Apr 2020), 736–747.DOI: Google ScholarCross Ref - [150] . 2021. Cross-lingual knowledge transferring by structural correspondence and space transfer. IEEE Transactions on Cybernetics 52, 7 (
Feb 2021), 1–12.DOI: Google ScholarCross Ref - [151] . 2018. Personalized microblog sentiment classification via adversarial cross-lingual multi-task learning. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 338–348.
DOI: Google ScholarCross Ref - [152] . 2018. Transition-based adversarial network for cross-lingual aspect extraction. In Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI-18) (IJCAI’18). AAAI Press, 4475–4481. Retrieved from Google ScholarDigital Library
- [153] . 2010. Cross lingual adaptation: An experiment on sentiment classifications. In Proceedings of the ACL 2010 Conference Short Papers (ACLShort’10). Association for Computational Linguistics, 258–262. Retrieved from Google ScholarDigital Library
- [154] . 2021. Reinforced transformer with cross-lingual distillation for cross-lingual aspect sentiment classification. Electronics 10, 3 (
Jan 2021), 270.DOI: Google ScholarCross Ref - [155] . 2010. Cross lingual opinion analysis via transfer learning. Australian Journal of Intelligent Information Processing Systems 11, 2 (
Jan 2010), 28.Google Scholar - [156] . 2020. Ensemble with estimation: Seeking for optimization in class noisy data. International Journal of Machine Learning and Cybernetics 11, 2 (2020), 231–248.Google ScholarCross Ref
- [157] . 2011. Instance level transfer learning for cross lingual opinion analysis. In Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis (WASSA 2011). Association for Computational Linguistics, 182–188. Retrieved from https://aclanthology.org/W11-1724Google ScholarDigital Library
- [158] . 2022. A survey of cross-lingual sentiment analysis: Methodologies, models and evaluations. Data Science and Engineering 7, 3 (
Jun 2022), 279–299.DOI: Google ScholarCross Ref - [159] . 2021. Multi-label sentiment analysis on 100 languages with dynamic weighting for label imbalance. IEEE Transactions on Neural Networks and Learning Systems 34, 1 (
Jul 2021), 1–11.DOI: Google ScholarCross Ref - [160] . 2020. Interactive refinement of cross-lingual word embeddings. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, 5984–5996.
DOI: Google ScholarCross Ref - [161] . 2010. Cross-lingual latent topic extraction. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL’10). Association for Computational Linguistics, 1128–1137. Retrieved from https://aclanthology.org/P10-1115Google ScholarDigital Library
- [162] . 2015. Cross-language sentiment classification algorithm based on dependency analysis parser and weight on property probability. Pattern Recognition and Artificial Intelligence 28, 11 (
Jan 2015), 1002–1012.DOI: Google ScholarCross Ref - [163] . 2018. Cross-lingual emotion classification with auxiliary and attention neural networks. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 429–441.
DOI: Google ScholarCross Ref - [164] . 2016. Cross-lingual sentiment classification: Similarity discovery plus training data adjustment. Knowledge-Based Systems 107, 2016 (
Sep 2016), 129–141.DOI: Google ScholarDigital Library - [165] . 2020. Gender bias in multilingual embeddings and cross-lingual transfer. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 2896–2907.
DOI: Google ScholarCross Ref - [166] . 2015. Learning translation models from monolingual continuous representations. In Proceedings of the 2015 International Conference on North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 1527–1536. Retrieved from https://aclanthology.org/N15-1176Google ScholarCross Ref
- [167] . 2022. Cross-lingual embeddings with auxiliary topic models. Expert Systems with Applications 190, 2022 (
Mar 2022), 116194.DOI: Google ScholarDigital Library - [168] . 2014. Bridging the language gap: Learning distributed semantics for cross-lingual sentiment classification. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 138–149.
DOI: Google ScholarCross Ref - [169] . 2015. A subspace learning framework for cross-lingual sentiment classification with partial parallel data. In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI’15). AAAI Press, 1426–1432. Retrieved from Google ScholarDigital Library
- [170] . 2014. Cross-lingual sentiment classification based on denoising autoencoder. In Proceedings of the CCF International Conference on Natural Language Processing and Chinese Computing. Springer, 181–192.
DOI: Google ScholarCross Ref - [171] . 2015. Learning bilingual sentiment word embeddings for cross-language sentiment classification. 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). Association for Computational Linguistics, 430–440.
DOI: Google ScholarCross Ref - [172] . 2016. Attention-based LSTM network for cross-lingual sentiment classification. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 247–256. Retrieved from https://aclanthology.org/D16-1024Google ScholarCross Ref
- [173] . 2016. Cross-lingual sentiment classification with bilingual document representation learning. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 1403–1412. Retrieved from https://aclanthology.org/P16-1133Google ScholarCross Ref
- [174] . 2023. Mix before align: Towards zero-shot cross-lingual sentiment analysis via soft-mix and multi-view learning. In Proceedings of the INTERSPEECH. 3969–3973.Google ScholarCross Ref
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