improving music mood classification using lyrics, audio and social tags

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in a study on music genre classification (Mayer et al., 2008). Besides, combinations of these feature types are also evaluated in this study and are described in this section as well. 6.2.1 Basic Lyric Features As a starting point, this research evaluates bag-of-words features with the following types: 1) Content words (Content): all words except function words, without stemming; 2) Content words with stemming (Cont-stem): stemming means combining words with the same roots; 3) Part-of-speech (POS) tags: such as noun, verb, proper noun, etc. In this research, the Stanford POS tagger 11 is used to tag each lyric word with one of the 36 unique POS tags in the Penn Treebank project 12 ; 4) Function words (FW): as opposed to content words, also called “stopwords” in text information retrieval. The function word list used in this study is the one compiled by S. Argamon, a well-known scholar in the area of text stylistic analysis 13 . For each of the feature types, four representation models are compared: 1) Boolean; 2) term frequency; 3) normalized frequency; and, 4) tfidf weighting. In a Boolean representation model, each feature value is term presence or absence (one or zero). The term frequency and normalized frequency models, as their names indicate, use term frequencies and normalized term frequencies 11 http://nlp.stanford.edu/software/tagger.shtml 12 http://www.cis.upenn.edu/~treebank/ 13 The function word list can be accessed at http://www.ir.iit.edu/~argamon/function-words.txt 71
as feature values respectively. The model of tfidf weighting uses the product of term frequency and inverted document frequency as feature values. The name “bag-of-words” simply means a collection of unordered terms, and the terms could be single words (also called “unigram”), POS tags, or ordered combinations of multiple words (also called “n-gram”). In this study, unigrams, bigrams and trigrams of the above features and representation models are all evaluated. For each n-gram feature type, features that occurred less than five times in the training dataset were discarded. In addition, for bigrams and trigrams of Content and Cont-stem, function words were not eliminated because content words are usually connected via function words as in “I love you,” where “I” and “you” are function words. For Cont-stem, words were stemmed before bigrams and trigrams were calculated. That is, every word in a bigram or trigram was stemmed. Theoretically, high order n-grams can capture features of phrases and compound words. A previous study on lyric mood classification (He et al., 2008) found the combination of unigrams, bigrams and trigrams yielded the best results among all n-gram features (n
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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
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 and 78: The patterns shown in this figure a
Page 79 and 80: CHAPTER 6: BEST LYRIC FEATURES This
Page 81: Liu (2006) identified discriminativ
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
Page 129 and 130: Byrd, D., & Crawford, T. (2002). Pr
Page 131 and 132: 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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