Can Social Comments Contribute to Estimate Impression of Music Video Clips?

The main objective of this paper is to estimate the impressions of music video clips using social comments to achieve impression-based music video clip searches or recommendation systems. To accomplish the objective, we generated a dataset that consisted of music video clips with evaluation scores on individual media and impression types. We then evaluated the precision with which each media and impression type were estimated by analyzing social comments. We also considered the possibility and limitations of using social comments to estimate impressions of content. As a result, we revealed that it is better to use proper parts-of-speech in social comments depending on each media/impression type.

[1]  Yuji Matsumoto,et al.  Applying Conditional Random Fields to Japanese Morphological Analysis , 2004, EMNLP.

[2]  Mert Bay,et al.  The 2007 MIREX Audio Mood Classification Task: Lessons Learned , 2008, ISMIR.

[3]  Frank Hopfgartner,et al.  Understanding Affective Content of Music Videos through Learned Representations , 2014, MMM.

[4]  Keith B. Hall,et al.  Improved video categorization from text metadata and user comments , 2011, SIGIR '11.

[5]  Masataka Goto,et al.  A chorus section detection method for musical audio signals and its application to a music listening station , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[6]  Wen Li,et al.  Exploiting User Comments for Audio-Visual Content Indexing and Retrieval , 2013, ECIR.

[7]  Touradj Ebrahimi,et al.  Multimedia content analysis for emotional characterization of music video clips , 2013, EURASIP J. Image Video Process..

[8]  Rongrong Ji,et al.  Video indexing and recommendation based on affective analysis of viewers , 2011, MM '11.

[9]  Hideki Kenmochi,et al.  VOCALOID - commercial singing synthesizer based on sample concatenation , 2007, INTERSPEECH.

[10]  Jens Grivolla,et al.  Multimodal Music Mood Classification Using Audio and Lyrics , 2008, 2008 Seventh International Conference on Machine Learning and Applications.

[11]  Takehiro Yamamoto,et al.  Leveraging viewer comments for mood classification of music video clips , 2013, SIGIR.

[12]  K. Hevner.  Experimental studies of the elements of expression in music , 1936 .

[13]  J. Russell.  A circumplex model of affect. , 1980 .

[14]  Andreas F. Ehmann,et al.  Lyric Text Mining in Music Mood Classification , 2009, ISMIR.