improving music mood classification using lyrics, audio and social tags

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compare the differences between lyrics and text in other genres, an initial examination of the lyric text suggests that the repetitions frequently used in lyrics indeed make a difference in stemming. For example, the lines, “bounce bounce bounce” and “but just bounce bounce bounce, yeah” were stemmed to “bounc bounc bounc” and “but just bounc bounc bounce, yeah.” The original bigram “bounce bounce” then expanded into two bigrams after stemming: “bounc bounc” and “bounc bounce” while the original trigram “bounce bounce bounce” also became two trigrams after stemming: “bounc bounc bounc” and “bounc bounc bounce.” Table 6.1 Summary of basic lyric features Feature Type n-grams No. of dimensions unigrams 7,227 Content words without stemming (Content) bigrams 34,133 trigrams 42,795 uni+bigrams 41,360 uni+bi+trigrams 84,155 unigrams 6,098 Content words with stemming (Cont-stem) bigrams 33,008 trigrams 42,707 uni+bigrams 39,106 uni+bi+trigrams 81,813 unigrams 36 Part-of-speech (POS) bigrams 1,057 trigrams 8,474 uni+bigrams 1,093 uni+bi+trigrams 9,567 unigrams 467 Function words (FW) bigrams 6,474 trigrams 8,289 uni+bigrams 6,941 uni+bi+trigrams 15,230 73
6.2.2 Linguistic Lyric Features In the realm of text sentiment analysis, domain dependent lexicons are often consulted in building feature sets. For example, Subasic and Huettner (2001) manually constructed a word lexicon with affective scores for each affect category considered in their study and classified documents by comparing the average scores of terms included in the lexicon. Pang and Lee (2008) summarized that studies on text sentiment analysis often used existing off-the-shelf lexicons. In this study, a range of psycholinguistic resources are exploited in extracting lyric features: General Inquirer (GI), WordNet, WordNet-Affect, and Affective Norms for English Words (ANEW). 6.2.2.1 Lyric Features based on General Inquirer General Inquirer (GI) is a psycholinguistic lexicon containing 8,315 unique English words and 182 psychological categories (Stone, 1966). Each sense of the 8,315 words in the lexicon is manually labeled, with one or more of the 182 psychological categories to which the sense belongs. For example, the word “happiness” is associated with the categories “Emotion,” “Pleasure,” “Positive,” “Psychological well being,” etc. The mapping between words and psychological categories provided by GI can be very helpful in looking beyond word forms and into word meanings, especially for affect analysis where a person’s psychological state is exactly the subject of study. One of the previous studies on music mood classification (Yang & Lee, 2004) used GI features together with lyric bag-of-words and suggested representative GI features for each of their six mood categories. 74
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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
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 and 82: Liu (2006) identified discriminativ
Page 83: as feature values respectively. The
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,
Page 133 and 134: 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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