improving music mood classification using lyrics, audio and social tags

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6.2.3 Text Stylistic Features Text stylistic features often refer to interjection words (e.g., “ooh,” “ah”), special punctuations (e.g., “!,” “?”) and text statistics (e.g., number of unique words, length of words, etc.). They have been used effectively in text stylometric analyses dealing with authorship attribution, text genre identification, and authority classification (Argamon et al., 2003). In the music domain, text stylistic features on lyrics were successfully used in a study in music genre classification (Mayer et al., 2008). In particular, Mayer et al. (2008) demonstrated interesting distribution patterns of some exemplar lyric features across different genres. For example, the word “nuh” and “fi” mostly occurred in reggae and hip-hop songs. Their experiment results showed that the combination of text stylistic features, part-of-speech features and audio spectral features significantly outperformed the classifier using audio spectral features only as well as the classifier combining audio and bag-of-words lyric features. In the task of mood classification, the usefulness of text stylistic features has not been formally evaluated, and thus this dissertation research includes text stylistic features. In particular, the text stylistic features evaluated in this study are defined in Table 6.2, which includes 25 dimensions: six interjection words, two special punctuation marks and 17 text statistics (also see Section 6.4.3). Table 6.3 summarizes the aforementioned linguistic features and text stylistic features with their numbers of dimensions and numbers of representation models. 77
Table 6.2 Text stylistic features evaluated in this research Feature Definition interjection words normalized frequencies of “hey,” “ooh,” “yo,” “uh,” “ah,” “yeah” special punctuation marks normalized frequencies of “!,” “-” NUMBER normalized frequency of all non-year numbers numberOfWords total number of words numberOfUniqWords total number of unique words repeatWordRatio (number of words - number of uniqWords)/number of words avgWordLength average number of characters per word numberOfLines total number of lines numberOfUniqLines total number of unique lines numberOfBlankLines number of blank lines blankLineRatio number of blankLines / number of lines avgLineLength number of words / number of lines stdLineLength standard deviation of number of words per line uniqWordsPerLine number of uniqWords / number of lines repeatLineRatio (number of lines – number of uniqLines) /number of lines avgRepeatWordRatioPerLine average repeat word ratio per line stdRepeatWordRatioPerLine standard deviation of repeat word ratio per line numberOfWordsPerMin number of words / song length in minutes numberOfLinesPerMin number of lines / song length in minutes Table 6.3 Summary of linguistic and stylistic lyric features Feature Number of Number of Feature Type Abbreviation dimensions representations GI GI psychological features 182 4 GI-lex words in GI 8,315 4 ANEW scores in expanded ANEW 12 1 Affect-lex words in expanded ANEW and WordNet-Affect 7,756 4 TextStyle text stylistic features 25 1 6.2.4 Lyric Feature Type Concatenations Combinations of different lyric feature types may yield performance improvements. For example, Mayer et al. (2008) found the combination of text stylistic features and part-of-speech 78
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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
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 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: mixed with regard to its usefulness
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
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
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48. Segment annotation plus specifi
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