improving music mood classification using lyrics, audio and social tags

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features achieved better classification performance than using either feature type alone. This dissertation research first determines the best representation of each feature type and then the best representations are concatenated with one another. Specifically, for the basic lyric feature types listed in Table 6.1, the best performing n-grams and representation of each type (i.e., content words, part-of-speech, and function words) is chosen and then further concatenated with linguistic and stylistic features. For each of the linguistic feature types with four representation models, the best representation is selected and then further concatenated with other feature types. In total, there are eight selected feature types: 1) n-grams of content word (either with or without stemming); 2) n-grams of part-of-speech; 3) n-grams of function words; 4) GI; 5) GI-lex; 6) ANEW; 7) Affect-lex; and, 8) TextStyle. The total number of feature type concatenations can be calculated as follows: 8 ∑ i= 1 i C 255 (1) 8 = where C denotes the combinations of choosing i types from all eight types (i = 1,…,8). All the 255 feature type concatenations as well as original feature types are compared in the experiments to find out which lyric feature type or concatenation of multiple types is the best for the task of music mood classification. 79
6.3 IMPLEMENTATION The Snowball stemmer 14 is used for experiments that require stemming. As this stemmer cannot handle irregular words, it is supplemented with irregular nouns and verbs 15 . The Stanford POS tagger implements two tagging models: one uses the preceding three tags as tagging context, the other considers both preceding and following tags (Toutanova, Klein, Manning, & Singer, 2003). The bidirectional model performs slightly better than the left sideonly model, but is significantly slower. As the lyric dataset used in this research is large, the more efficient left side-only model is adopted. The Stanford tagger is trained on a corpus consisting of articles in the Wall Street Journal. News articles are in a different text genre from lyrics, but there is no available training corpus of lyrics with annotated POS tags. Nevertheless, the lyric data are also in modern English, and the combinations of POS tags in lyrics are not much different from news articles. The author has manually examined about 50 tagged lyrics and the results are generally correct. 6.4 RESULTS AND ANALYSIS 6.4.1 Best Individual Lyric Feature Types For the basic lyric features summarized in Table 6.1, the variations of uni+bi+trigrams in the Boolean representation worked best for all three feature types (content words, part-of-speech, and function words). Stemming did not make a significant difference on the performances of 14 http://snowball.tartarus.org/ 15 The irregular verb list was obtained from http://www.englishpage.com/irregularverbs/irregularverbs.html, and the irregular noun list was obtained from http://www.esldesk.com/esl-quizzes/irregular-nouns/irregular-nouns.htm 80
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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
Page 39 and 40: previous research did, Bischoff et
Page 41 and 42: other. Stylistic features, borrowed
Page 43 and 44: 2.5 SUMMARY This chapter provided a
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: Table 6.2 Text stylistic features e
Page 93 and 94: shows the best combined feature set
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
Page 107 and 108: systems and the audio-only system w
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 and 126: close relationship with music mood.
Page 127 and 128: available, it is interesting to exp
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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