improving music mood classification using lyrics, audio and social tags

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There are several operational systems focused on text categorization according to affect. Subasic and Huettner (2001) manually constructed a word lexicon for each affect category considered in their study, and classified documents by comparing the average scores of terms in the affect categories. A more complex approach was taken by Liu, Lieberman, and Selker (2003) which was based on common sense knowledge, due to the assumption that common sense is important for affect interpretation. As lyrics are a special genre quite different from daily life documents, a common sense knowledge base may not work for lyrics; neither do word lexicons built for other genres of documents. While manually building a lexicon is very labor-intensive, methods on automatic lexicon induction have been proposed. Pang and Lee (2008) summarized such methods and categorized them into two groups: unsupervised and supervised. The three feature selection methods applied to lyrics in Hu et al. (2009a) (e.g., language model comparison, F-score feature ranking, and SVM feature ranking) are vivid examples of supervised lexicon induction. Unsupervised methods start from a few seed words for which the affect is already known, and then propagate the labels of the seed words to words that co-occur with them in a text corpus, to synonyms, and/or to words that co-occur with them in other resources like WordNet. For instance, Turney (2002) proposed the joint use of mutual information and co-occurrence in a general corpus with a small set of seed words. There have been interesting studies on the affective aspect of text in the context of weblogs (Nicolov, Salvetti, Liberman, & Martin, 2006). Most of them still used bag-of-words features of all words or words in specific POSs (mostly adjectives and nouns). Among them, Mihalcea and 69
Liu (2006) identified discriminative words in blog posts in two categories, “happy” and “sad,” using Naïve Bayesian classifiers and word frequency threshold. Alm (2008) studied affects of sentences in children’s tales and included a very rich feature set covering the aspects of syntactic (e.g., POS ratios, interjection word count), rhetoric (e.g., repetitions, onomatopoeia counts), lexical (counts of words in pre-built, emotion-related word lists), and orthographic (e.g., special punctuations). Although the affect categories in Alm’s study were not from a dimensional model, Alm included in the feature set dimensional lexical scores calculated from the ANEW word list (Bradley & Lang, 1999). ANEW stands for Affective Norms for English Words. It contains 1,034 unique words with scores in three dimensions: valence (a scale from unpleasant to pleasant), arousal (a scale from calm to excited), and dominance (a scale from submissive to dominant). All dimensions are scored on a scale of 1 to 9. Alm’s features used the average scores of word hits in the ANEW list. Unfortunately, Alm did not evaluate which of these features were most useful in predicting affect categories. However, Alm’s study, among the few studies on text affect prediction, does suggest possible features for consideration in this research. 6.2 LYRIC FEATURES Based on the aforementioned studies on text affect analysis, this dissertation research investigates a range of lyric feature types that can be categorized into the following three classes: 1) basic text features that are commonly used in text categorization tasks; 2) linguistic features based on psycholinguistic resources; and, 3) text stylistic features including those proven useful 70
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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
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 and 78: The patterns shown in this figure a
Page 79: CHAPTER 6: BEST LYRIC FEATURES This
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
Page 129 and 130: 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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