improving music mood classification using lyrics, audio and social tags

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2.4.2 Social Tags Very recently, the increasing number of musical social tags on the Web has stimulated great interest in analyzing and exploiting social tags in MIR. Geleijnse et al. (2007) investigated social tags associated with 224 artists and their famous tracks on last.fm and used the tags to create a ground truth set of artist similarity. Levy and Sandler (2007) analyzed track tags published on last.fm and mystrands.com and concluded social tags were effective in capturing music similarity. Using tags on artist, album and tracks provided by last.fm, Hu et al. (2007a) derived a set of music mood categories as well as a ground truth track set corresponding to these categories. Symeonidis, Ruxanda, Nanopoulos, and Manolopoulos (2008) again exploited last.fm tags, but considered one more dimension – the users, for personalized music recommendations. Other research attempted to link social tags to audio content. Eck, Bertin-Mahieux, and Lamere (2007) proposed a method of predicting social tags from audio input using supervised learning. Their dataset was social tags applied to nearly 100,000 artists obtained from last.fm. Indeed, social tags have become so popular in the MIR community that since 2008, the MIREX has added a new task, Audio Tag Classification 5 , which compares various systems with regard to the abilities of associating 10-second audio clips of music with tags collected from the MajorMiner 6 game (Mandel & Ellis, 2007). 5 http://www.music-ir.org/mirex/wiki/2008:Audio_Tag_Classification 6 http://majorminer.com/ 31
2.5 SUMMARY This chapter provided a brief overview of music mood as a newly emerging music metadata type and, at the same time, a well-studied subject in music psychology. This chapter also reviewed important findings in music psychology which could benefit information scientist and MIR researchers. In particular, two influential music emotion models were introduced in detail. These models will be used for comparisons in the next chapter. By reviewing the various approaches to music mood classification and the applications of lyrics and social tags in MIR, this chapter also presented the context for the research questions raised in this dissertation. 32
Page 1 and 2: IMPROVING MUSIC MOOD CLASSIFICATION
Page 3 and 4: concatenation and late fusion (line
Page 5 and 6: ACKNOWLEDGMENTS I would have never
Page 7 and 8: TABLE OF CONTENTS LIST OF FIGURES .
Page 9 and 10: 7.2.2 Best Hybrid Method ..........
Page 11 and 12: LIST OF TABLES Table 3.1 Mood categ
Page 13 and 14: music itself and creating the socia
Page 15 and 16: the ultimate judge. Thus ground tru
Page 17 and 18: The advantages have attracted resea
Page 19 and 20: Research Question 2: Which type(s)
Page 21 and 22: There are two popular approaches in
Page 23 and 24: 1.4.1 Contributions to Methodology
Page 25 and 26: experimental datasets in music mood
Page 27 and 28: CHAPTER 2: LITERATURE REVIEW 2.1 MO
Page 29 and 30: hypothesizes that there are at leas
Page 31 and 32: 2.2.3 What We Know about Music Mood
Page 33 and 34: encoding facial expressions, some o
Page 35 and 36: environment by real-life users. The
Page 37 and 38: 1) Tempo: also called “rhythmic p
Page 39 and 40: previous research did, Bischoff et
Page 41: other. Stylistic features, borrowed
Page 45 and 46: “evaluation” containing 492 wor
Page 47 and 48: which it is derived. For instance,
Page 49 and 50: 3.3 COMPARISONS TO MUSIC PSYCHOLOGY
Page 51 and 52: In total, 20 of the 36 derived cate
Page 53 and 54: in today’s music listening enviro
Page 55 and 56: 3.4 SUMMARY The identified categori
Page 57 and 58: CHAPTER 4: CLASSIFICATION EXPERIMEN
Page 59 and 60: accuracy = TP + TN TP + FN + FP + T
Page 61 and 62: Support Vector Machines (SVM), etc.
Page 63 and 64: 4.3 SUMMARY This chapter described
Page 65 and 66: Table 5.1 Information of audio coll
Page 67 and 68: irrelevant parts such as HTML marku
Page 69 and 70: Starting from the dataset collected
Page 71 and 72: Table 5.3 Mood categories and song
Page 73 and 74: 1) a song has been tagged with one
Page 75 and 76: 5.2.2.4 Negative Samples Figure 5.1
Page 77 and 78: The patterns shown in this figure a
Page 79 and 80: CHAPTER 6: BEST LYRIC FEATURES This
Page 81 and 82: Liu (2006) identified discriminativ
Page 83 and 84: as feature values respectively. The
Page 85 and 86: 6.2.2 Linguistic Lyric Features In
Page 87 and 88: mixed with regard to its usefulness
Page 89 and 90: Table 6.2 Text stylistic features e
Page 91 and 92: 6.3 IMPLEMENTATION The Snowball ste
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shows the best combined feature set
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6.4.3 Analysis of Text Stylistic Fe
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Figure 6.1 Distributions of “!”
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stylistic features appeared to be a
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Support Vector Machines (SVM) as it
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then compared to the feature concat
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According to the average accuracies
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systems and the audio-only system w
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Table 7.4 Accuracies of lyric and a
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7.4.2 Top-Ranked Features Based on
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Again, these top-ranked features se
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However, some might argue that word
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Figure 8.1 shows a general trend th
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There are other interesting observa
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CHAPTER 9: CONCLUSIONS AND FUTURE R
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different strengths. Lyric-based sy
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close relationship with music mood.
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available, it is interesting to exp
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Byrd, D., & Crawford, T. (2002). Pr
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Hu, X., Sanghvi, V., Vong, B., On,
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McEnnis, D., McKay, C., Fujinaga, I
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Thayer, R. E. (1989). The Biopsycho
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18. “music fade” at end of a se
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48. Segment annotation plus specifi
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improving music mood classification using lyrics, audio and social tags

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