improving music mood classification using lyrics, audio and social tags

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7.4.1 Top Features in Content Word N-Grams There are six categories where Content n-gram features significantly outperformed audio features. Table 7.5 lists the top-ranked content word features in these categories. Note how “love” seems an eternal topic of music regardless of the mood category! Highly ranked content words seem to have intuitively meaningful connections to the categories, such as “with you” in “romantic” songs, “happy” in “cheerful” songs, and “dreams” in “hopeful” songs. The categories, “angry,” “aggressive,” and “anxious” share quite a few top-ranked terms highlighting their emotional similarities. It is interesting to note that these last three categories sit closely in the same top-left quadrant in Figure 5.2. Table 7.5 Top-ranked content word features for categories where content words significantly outperformed audio romantic cheerful hopeful angry aggressive anxious with you i love you ll baby fuck hey on me night strong i am dead to you with your ve got i get shit i am change crazy happy loving scream girl left come on for you dreams to you man fuck i said new i ll run kill i know burn care if you shut baby dead hate for me to be i can love and if kiss living god control hurt wait let me rest lonely don t know but you waiting hold and now friend dead fear need to die all around dream love don t i don t why you heaven in the eye hell pain i m i ll met coming fighting lost listen tonight she says want hurt you i ve never again and i want you ve got wonder kill hate but you love more than waiting if you want have you my heart give me the sun i love oh baby love you hurt cry you like you best you re my yeah yeah night 99
7.4.2 Top-Ranked Features Based on General Inquirer Table 7.6 lists the top GI features for “aggressive”, the only category where the GI set of 182 psychological features significantly outperformed audio. Table 7.7 presents top GI word features in the four categories where “GI-lex” features significantly outperformed audio features. Table 7.6 Top GI features for “aggressive” mood category GI Feature Words connoting the physical aspects of well being, including its absence Words referring to the perceptual process of recognizing or identifying something by means of the senses Action words Words indicating time Words referring to all human collectivities Words related to a loss in a state of well being, including being upset Example Words blood, dead, drunk, fever, pain, sick, tired dazzle, fantasy, hear, look, make, tell, view hit, kick, drag, upset noon, night, midnight people, gang, party burn, die, hurt, mad Table 7.7 Top-ranked GI-lex features for categories where GI-lex significantly outperformed audio romantic aggressive hopeful exciting paradise baby i’m come existence fuck been now hit let would see hate am what up sympathy hurt do will jealous girl in tear kill be lonely bounce young another saw to destiny need like him found kill strong better anywhere can there shake soul but run everything swear just will us divine because found gonna across man when her clue one come free rascal dead lose me tale alone think more crazy why mine keep 100
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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
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
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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
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: Table 7.4 Accuracies of lyric and a
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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