improving music mood classification using lyrics, audio and social tags

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9.2 LIMITATIONS OF THIS RESEARCH 9.2.1 Music Diversity In the process of data collection, it is noteworthy that the dataset used in this research consists of popular vocal music with lyrics in English. As the social tags used in this research are solely provided by last.fm, they are naturally limited by the user population of last.fm. The demographic statistics of last.fm users 17 shows most users are in Western countries such as the United States, Britain, Germany and Poland. Also, last.fm users tend to be young and proficient with computers. Over 90% of its European users are from 16 to 34 years old, which at least partially explains that the most popular tags in last.fm are “rock” and “pop.” Therefore, the dataset of this research is limited to popular Western vocal music with lyrics in English, and the mood categories and ground truth labels are biased to young Western listeners. Thus the conclusions of this research are only applicable to this kind of music, and further exploration is needed for music from other culture backgrounds, languages and other groups of users. 9.2.2 Methods and Techniques Besides the models and techniques adopted in this research, there are other methods that might provide insights from different angles. For example, SVM is discriminative in contrast to generative models which have also been popular in both text mining (Naïve Bayesian model) and MIR (Gaussian models). In addition, some suboptimal audio features may yield better results when combined with text features. The spectral features used in this research achieved the best performance in MIREX, but other audio features (e.g., rhythm features) are expected to have a 17 http://socialmediastatistics.wikidot.com/lastfm latest updated on 22 Jul 2008. 113
close relationship with music mood. Future research may look into those features. Finally, dimension reduction techniques other than multidimensional scaling, such as Latent Semantic Analysis (LSA) and Principal Component Analysis (PCA) can be applied to analyzing mood categories, providing additional views on empirical music listening data. 9.3 FUTURE RESEARCH This research analyzed the general trends and results of improving music mood classification by combining lyric, audio and social tags. While it answered the formulated research questions, it raised even more questions for future research. 9.3.1 Feature Ranking and Selection This research has discovered many top performing lyric feature combinations are of high dimensionality. Feature selection has great potential to further improve performance. In Section 7.4, top features in individual lyric feature types were examined. The author plans to systematically analyze features in combined feature spaces such as GI + TextStyle. In addition, it is observed that no lyric-based feature provided significant improvements in the bottom-left (negative valence, negative arousal) quadrant in Figure 5.2 while audio features performed relatively well (i.e., “calm”). It is worthy of further study whether feature selection could improve classification on these categories. 9.3.2 More Classification Models and Audio Features The interaction of features and classifiers is worthy of further investigation. Using classification models other than SVM (e.g., Naïve Bayes), the top-ranked features might be 114
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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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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
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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 “!”
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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
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Page 121 and 122: CHAPTER 9: CONCLUSIONS AND FUTURE R
Page 123: different strengths. Lyric-based sy
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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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