计算机科学 ›› 2021, Vol. 48 ›› Issue (11): 312-318.doi: 10.11896/jsjkx.200900088
徐琳宏1, 刘鑫1, 原伟2, 祁瑞华1
XU Lin-hong1, LIU Xin1, YUAN Wei2, QI Rui-hua1
摘要： 俄语的多模态情感分析技术是情感分析领域的研究热点,它可以通过文本、语音和图像等丰富信息自动分析和识别情感,有助于及时了解俄语区民众和国家的舆论热点。但目前俄语的多模态情感语料库还较少,因而制约了俄语情感分析技术的进一步发展。针对该问题,在分析多模态情感语料库的相关研究及情感分类方法的基础上,首先制定了一套科学完整的标注体系,标注内容包括话语、时空和情感3个部分的11项信息;然后在语料库的整个建设和质量监控过程中,遵循情感主体原则和情感连续性原则,拟订出操作性较强的标注规范,进而构建出规模较大的俄语多模态情感语料库;最后探讨了语料库在解析情感表达特点、分析人物性格特征和构造情感识别模型等多个方面的应用。
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| [1]BUSSO C,BULUT M,LEE C,et al.IEMOCAP:Interactiveemotional dyadic motion capture database[J].Journal of Language Resources and Evaluation,2008,42(4):335-359. [2]DARIO B,PASCALE F.Deep Learning of Audio and Language Features for Humor Prediction[C]//Proceedings of the 10th International Conference on Language Resources and Evaluation.LREC,2016:496-501. [3]BERTERO D,PASCALE F.Predicting humor response in dialogues from TV sitcoms[C]//IEEE International Conference on Acoustics,Speech and Signal Processing (ICASSP).2016:5780-5784. [4]BERTERO D,FUNG P.A Long Short-Term Memory Frame-work for Predicting Humor in Dialogues[C]//Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:130-135. [5]HSU C C,KUO C C,CHEN S,et al.Emotionlines:An emotion corpus of multi-party conversations[C]//Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC).2018:1597-1601. [6]PORIA S,HAZARIKA D,MAJUMDER N,et al.MELD:A Multimodal Multi Party-Dataset for Emotion Recognitionin Conversations[C]//Annual Meeting of the Association for Computational Linguistics.2019:527-536. [7]WOLLMER M,WENINGER F,KNAUP T,et al.YouTubeMovie Reviews:Sentiment Analysis in an Audio-Visual Context[J].IEEE Intelligent Systems,2013,28(3):46-53. [8]PORIA S,CAMBRIA E,HOWARD N,et al.Fusing audio,visual and textual clues for sentiment analysis from multimodal content[J].Neurocomputing,2016,174(JAN.22PT.A):50-59. [9]ZADEH A,ZELLERS R,PINCUS E,et al.Mosi:Multimodal corpus of sentiment intensity and subjectivity analysis in online opinion videos[J].arXiv:1606.06259,2016. [10]ZADEH A,LIANG P P,POIRA S,et al.Multimodal Language Analysis in the Wild:CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph[C]//Annual Meeting of the Association for Computational Linguistics.2018:2236-2246. [11]WANG X M,ZHAO X B.Survey of Construction and Application of Reading Eye-tracking Corpus[J].Computer Science,2020,47(3):174-181. [12]MAKAROVA V,PERTRUSHIN V A.RUSLANA:a Database of Russian Emotional Utterances[C]//International Conference on Spoken Language Processing.2002:1-4. [13]COWIE R,DOUGLAS E,TSAPATSOULIS N,et al.Emotion recognition in human-computer interaction[J].IEEE Signal Processing Magazine,2002,18(1):32-80. [14]PEREPELKINA O,KAZIMIROVA E,KONSTANTINOVAM.RAMAS:Russian Multimodal Corpus of Dyadic Interaction for Affective Computing[C]//SPECOM.2018:1-6. [15]CARLETTA J.Assessing agreement on classification tasks:the kappa statistic[J].Computational Linguistics,1996,22(2):249-254. [16]LI X,LI Y,WANG S G.Text Similarity Calculation for Text Sentiment Clustering[J].Journal of Chinese Information Processing,2018,32(5):97-104. [17]WANG J C,XU Y,LIU Q Y,et al.Dialog Sentiment Analysis with Neural Topic Model[J].Journal of Chinese Information Processing,2020,34(1):106-112. [18]WANG K,PAN W,YANG B H.OTSRM-Based Approach for Sentiment Evolution and Topic Analysis[J].Journal of the China Society for Scientific and Technical Information,2019,38(5):534-542. [19]YANG L,ZHOU F Q,LIN H F,et al.Sentiment Analysis Based on Emotion Commonsense Knowledge[J].Journal of Chinese Information Processing,2019,33(6):94-99. [20]SAKENOVICH N S,ZHARMAGAMBETOV A S.On one approach of solving sentiment analysis task for Kazakh and Russian languages using deep learning[C]//International Confe-rence on Computational Collective Intelligence.2016:537-545. [21]GALINSKY R,ALEKSEEV A,NIKOLENKO S I.Improvingneural network models for natural language processing in Russian with synonyms[C]//IEEE Artificial Intelligence and Natural Language Conference.2016:1-7. [22]ZADEH A,LIANG P P,MAZUMDER N,et al.Memory Fusion Network for Multi-view Sequential Learning[J/OL].Association for the Advancement of Artificial Intelligence, 2018:5634-5641.https://arxiv.org/abs/1802.00927. [23]MAJUMDER N,PORIA S,HAZARIKA D,et al.Dialogue-RNN:An Attentive RNN for Emotion Detection in Conversations[C]//Association for the Advancement of Artificial Intelligence.2019:681-682. |
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