Multimodal sentiment analysis of Telugu songs

CEUR Workshop Proceedings

Volumn 1619, Issue , 2016, Pages 48-52

  Abburi, Harika ^a   Akkireddy, Eswar Sai Akhil ^a   Gangashetty, Suryakanth V ^a   Mamidi, Radhika ^a  

^a INTERNATIONAL INSTITUTE OF INFORMATION TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING;

AUDIO FEATURES; CHROMA FEATURES; GAUSSIAN MIXTURE MODEL; PROSODY FEATURES; SENTIMENT ANALYSIS; SINGLE VECTORS; SPECTRAL FEATURE; TEMPORAL FEATURES;

SUPPORT VECTOR MACHINES;

EID: 84984596852     PISSN: 16130073     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. [Campbel et al., 2006] MWilliam Campbel, P Joseph Cambell, A Douglas Reynolds, Elliot Singer, and A Pedro Torres-Carrasquillo. Support vector machines for speaker and language recognition. Computer Speech & Language, 20(2): 210-229, 2006.
2. [Dong et al., 2010] Zhendong Dong, Qiang Dong, and Changling Hao. Hownet and its computation of meaning. In Proc. 23rd international conference on computational linguistics: Demonstrations, association for computational linguistic, pages 53-56, 2010.
3. [Ellis, 2007] D. P.W. Ellis. Clasifying music audio with timbral and chroma features. In Proc. 8th Int. Conf.Music Inf. Retrieval (ISMIR), pages 339-340, 2007.
4. [Eyben et al., 2010] F. Eyben, M. Wollmer, and B. Schulle. opensmile themunich versatile and fast open-source audio feature extractor. In Proc. ACM Multimedia (MM), pages 1459-1462, 2010.
5. [Hu and Downie, 2010] X. Hu and J. S. Downie. Improving mood classification in music digital libraries by combining lyrics and audio. In Proc. Joint Conference on Digital Libraries, (JCDL), pages 159-168, 2010.
6. [Hu et al., 2007] X. Hu, M. Bay, and J. S. Downie. Creating a simplifiedmusic mood classification ground-Truthset. In Proc. 8th International Conference on Music Information Retrieval, 2007.
7. [Hu et al., 2009] Xiao Hu, J. Stephen Downie, and Andreas F. Ehmann. Lyric text mining in music mood classification. In Proc. 10th International Conference on Music Information Retrieval (ISMIR), pages 411-416, 2009.
8. [Jamdar et al., 2015] Adit Jamdar, Jessica Abraham, Karishma Khanna, and Rahul Dubey. Emotion analysis of songs based on lyrical and audio features. International Journal of Artificial Intelligence and Applications(IJAIA), 6(3): 35-50, 2015.
9. [Kaushik et al., 2013] Lakshmish Kaushik, Abhijeet Sangwan, and John H L. Hansen. Sentiment extraction from natural audio streams. In proc. ICASSP, pages 8485-8489, 2013.
10. [Kaushik et al., 2015] Lakshmish Kaushik, Abhijeet Sangwan, and John H.L. Hansen. Automatic audio sentiment extraction using keyword spotting. In Proc. INTERSPEECH, pages 2709-2713,September 2015.
11. [Mairesse et al., 2012] F. Mairesse, J. Polifroni, and G. Di Fabbrizio. Can prosody inform sentiment analysis experiments on short spoken reviews. In Proc. IEEE Int. Conf. Acoust., Speech, Signal Processing, pages 5093-5096, 2012.
12. [Medhat et al., 2014] Walaa Medhat, Ahmed Hassan, and Hoda Korashy. Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering journal, pages 1093-1113, 2014.
13. [Patra et al., 2013] B. G. Patra, D. Das, and S. Bandyopadhyay. Unsupervised approach to hindi music mood classification. In Mining Intelligence and Knowledge Exploration (MIKE 2013), R. Prasath and T. Kathirvalavakumar (Eds.): LNAI 8284, pages 62-69, 2013. Springer International Publishing.
14. [Reynolds and Rose, 1995]A Douglas Reynolds and C Richard Rose. Robust text-independent speaker identification using gaussian mixture speaker models. IEEE Transactions on Speech and Audio Processing, 3(1): 72-83, 1995.
15. [Rish, 2001] Irina Rish. An empirical study of the naive bayes classifier. In Proc. IJCAI-01 Workshop on Empirical Methods in Artificial Intelligence, 2001.
16. [Shanmugapriya and Dr.B.Srinivasan, 2015] K.P Shanmugapriya and Dr.B.Srinivasan. An efficient method for determining sentiment from song lyrics based on wordnet representation using hmm. International Journal of Innovative Research in Computer and Communication Engineering, 3(2): 1139-1145,February 2015.
17. [Tyagi and Chandra, 2015] Atul Tyagi and Nidhi Chandra. An introduction to the world of sentimnt analysis. In Proc. 28th IRF International Conference, June 2015.
18. [Wang et al., 2009] Tao Wang, DongJu Kim, KwangSeok Hong, and JehSeon Youn. Music information retrieval system using lyrics andmelody information. In proc. Asia-Pacific Conference on Information Processing, pages 601-604, 2009.
19. [Xia et al., 2008] Yunqing Xia, Linlin Wang, Kam-Fai Wong, and Mingxing Xu. Sentiment vector space model for lyric-based song sentiment classification. In proc. ACL-08: HLT, Short Papers, pages 133-136, 2008.
20. [Zhong et al., 2012] Jiang Zhong, Yifeng Cheng, Siyuan Yang, and Luosheng Wen. Music sentiment classification integrating audio with lyrics. Information and Computational Science, 9(1): 35-54, 2012.