improving music mood classification using lyrics, audio and social tags

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different than those selected by SVM. In addition, novel and high level audio features have been recently proposed such as “danceability” (Laurier et al., 2008) which measures how likely listeners would dance with the music. Combining lyrics with those new audio features may further improve classification performance. 9.3.3 Enrich Music Mood Theories This study has identified mood categories from social tags and presented an empirical and real-life case for the reference of music psychologists. In the future, the author will strive to identify more patterns in people’s music listening behaviors from social media data, find connections between these patterns and music psychology theories, and offer suggestions and insights to music psychology research. An example of this type of research question would be the degree of moods in individual music pieces. In this research, the membership to each mood category is binary. That is, a song either belongs to a category or not. In the reality, songs often show a combination of moods with certain degrees, such as “a mostly calm song with a bit of sadness.” Besides, other related topics include the taxonomy of degrees, definition of correctness, differentiation of two types of errors (i.e., false positive and false negative), etc. Once discovered, the findings will help enrich and extend the theories on music mood. 9.3.4 Music of Other Types and in Other Cultures Due to data availability, this study focuses on popular English songs. Other types of music such as classical music have long been studied by music psychologists. When data become 115
available, it is interesting to explore whether the same methods used in this study can be reliably applied to other music types that have both audio and lyrics parts (e.g., classical opera songs). Music is culturally dependent. Conclusions drawn from music in one culture may be radically different from those in another. This research focused on popular English songs, and thus it would be interesting to extend this research to popular music in another culture, like Chinese and Spanish songs. Comparisons on findings will be instructive for designing crossculture music repositories and services. 9.3.5 Other Music-related Social Media Than Social Tags With the advent of Web 2.0, there is a large and growing amount of user generated data available online such as social tags, blogs, microblogs, customer reviews, etc. Such data provide first-hand resources for studying and understanding users in daily life settings. This study only exploits social tags on music materials. In the future, music blogs, playlists, and other musicrelated information published by users on social media websites can also be exploited. 116
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IMPROVING MUSIC MOOD CLASSIFICATION
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concatenation and late fusion (line
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ACKNOWLEDGMENTS I would have never
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TABLE OF CONTENTS LIST OF FIGURES .
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7.2.2 Best Hybrid Method ..........
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LIST OF TABLES Table 3.1 Mood categ
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music itself and creating the socia
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the ultimate judge. Thus ground tru
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The advantages have attracted resea
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Research Question 2: Which type(s)
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There are two popular approaches in
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1.4.1 Contributions to Methodology
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experimental datasets in music mood
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CHAPTER 2: LITERATURE REVIEW 2.1 MO
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hypothesizes that there are at leas
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2.2.3 What We Know about Music Mood
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encoding facial expressions, some o
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environment by real-life users. The
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1) Tempo: also called “rhythmic p
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previous research did, Bischoff et
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other. Stylistic features, borrowed
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2.5 SUMMARY This chapter provided a
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“evaluation” containing 492 wor
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which it is derived. For instance,
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3.3 COMPARISONS TO MUSIC PSYCHOLOGY
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In total, 20 of the 36 derived cate
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in today’s music listening enviro
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3.4 SUMMARY The identified categori
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CHAPTER 4: CLASSIFICATION EXPERIMEN
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accuracy = TP + TN TP + FN + FP + T
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Support Vector Machines (SVM), etc.
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4.3 SUMMARY This chapter described
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Table 5.1 Information of audio coll
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irrelevant parts such as HTML marku
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Starting from the dataset collected
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Table 5.3 Mood categories and song
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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
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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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Page 95 and 96: 6.4.3 Analysis of Text Stylistic Fe
Page 97 and 98: Figure 6.1 Distributions of “!”
Page 99 and 100: stylistic features appeared to be a
Page 101 and 102: Support Vector Machines (SVM) as it
Page 103 and 104: then compared to the feature concat
Page 105 and 106: According to the average accuracies
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Page 109 and 110: Table 7.4 Accuracies of lyric and a
Page 111 and 112: 7.4.2 Top-Ranked Features Based on
Page 113 and 114: Again, these top-ranked features se
Page 115 and 116: However, some might argue that word
Page 117 and 118: Figure 8.1 shows a general trend th
Page 119 and 120: There are other interesting observa
Page 121 and 122: CHAPTER 9: CONCLUSIONS AND FUTURE R
Page 123 and 124: different strengths. Lyric-based sy
Page 125: close relationship with music mood.
Page 129 and 130: Byrd, D., & Crawford, T. (2002). Pr
Page 131 and 132: Hu, X., Sanghvi, V., Vong, B., On,
Page 133 and 134: McEnnis, D., McKay, C., Fujinaga, I
Page 135 and 136: Thayer, R. E. (1989). The Biopsycho
Page 137 and 138: 18. “music fade” at end of a se
Page 139 and 140: 48. Segment annotation plus specifi
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