improving music mood classification using lyrics, audio and social tags

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common knowledge on music. For example, all Metal songs are in negative moods, particularly “aggressive” and “angry” while 40% of Electronic examples are associated with category “calm.” Moreover, it is not surprising that most New Age examples are labeled with the moods of “calm,” “sad,” and “dreamy.” Table 5.6 Genre and mood distribution of positive examples Electronic Metal New Age R & B Hip Hop Rock Other Unknown total Blues Oldies Jazz Reggae Country calm 2 2 9 40 0 24 15 34 21 29 1,239 8 257 1,680 sad 1 1 1 11 4 9 9 3 19 10 907 12 191 1,178 glad 1 3 8 17 0 4 9 10 8 7 466 3 213 749 romantic 0 2 5 3 0 6 13 2 15 1 477 9 86 619 gleeful 0 0 0 11 0 2 4 2 12 11 366 2 133 543 gloomy 3 0 1 4 6 0 0 1 3 15 390 2 46 471 angry 0 0 0 1 9 0 0 0 0 9 187 0 48 254 mournful 0 0 0 1 0 0 2 0 13 3 130 1 33 183 dreamy 0 0 0 5 0 9 0 0 0 0 105 0 27 146 cheerful 0 3 1 3 0 1 1 1 2 2 101 3 24 142 brooding 0 0 0 2 0 2 1 0 0 0 90 0 21 116 aggressive 0 0 0 2 14 0 0 0 0 5 82 0 12 115 anxious 0 0 0 1 1 0 0 0 2 0 70 0 6 80 confident 0 0 0 2 0 0 0 1 1 2 43 1 11 61 hopeful 0 0 0 2 0 0 0 0 1 0 32 1 9 45 earnest 0 0 0 0 0 2 0 0 1 0 35 0 2 40 cynical 0 0 0 0 0 0 0 0 0 1 35 0 2 38 exciting 0 0 0 1 0 0 0 0 0 1 27 0 1 30 TOTAL 7 11 25 106 34 59 54 54 98 96 4,782 42 1,122 6,490 67
CHAPTER 6: BEST LYRIC FEATURES This chapter presents the experiments and results that aim to answer research question 2: which type(s) of lyric features are the most useful in classifying music by mood. Starting with an overview of the state of the art in text affect analysis, the chapter then describes the lyric features investigated in this research and finally presents the results and discussions. 6.1 TEXT AFFECT ANALYSIS Pang and Lee (2008) recently published a comprehensive survey on sentiment analysis in text. By sentiment analysis, they mainly referred to analysis on subjectivity, sentimental polarity (negative vs. positive), and political viewpoints (liberal vs. conservative). They summarized the features that have been used in sentiment analysis: bag-of-words (in pre-built lexicons), part-ofspeech (POS) tags, position in the document, higher-order n-grams, dependency or constituentbased features. However, which features are most useful depends on specific tasks. Moreover, as sentiment analysis is a relatively new area, it is still too early to make assertions on features. A related area is stylometric analysis, which usually refers to authorship attribution, text genre identification, and authority classification. Previous studies on stylometric analysis have shown that statistical measures on text properties (e.g., word length, punctuation and function words, contractions, named entities, non-standard spellings) could be very useful (e.g., Argamon, Saric, & Stein, 2003; Hu, Downie, & Ehmann, 2007b). Over one thousand stylometric features have been proposed in a variety of research (Rudman, 1998). However, there is no agreement on the best set of features for a wide range of application domains. 68
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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
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 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: The patterns shown in this figure a
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
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
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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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