improving music mood classification using lyrics, audio and social tags

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CHAPTER 3: MOOD CATEGORIES IN SOCIAL TAGS This chapter describes the method and results of identifying mood categories from social tags on last.fm. It also presents result analysis on comparing the identified categories to models in music psychology. Research question 1, whether social tags can be used to identify a reasonable set of mood categories, is answered with the findings. 3.1 IDENTIFYING MOOD CATEGORIES FROM SOCIAL TAGS A new method is proposed to derive mood categories from social tags by combining the strength of linguistic resources and human expertise. This section describes this method in detail. 3.1.1 Identifying Mood-related Terms First, a set of mood-related terms are identified using linguistic resources. WordNet-Affect (Strapparava & Valitutti, 2004) is an affective extension of WordNet (Fellbaum, 1998). It assigns affective labels to words representing emotions, moods, situations eliciting emotions, or emotional responses. As a major resource used in text sentiment analysis, WordNet-Affect has a good coverage of mood-related words. There are 1,586 terms in WordNet-Affect, but some of them are judgmental, such as “bad,” “poor,” “miserable,” “good,” “great,” and “amazing.” Although these terms are related to mood, their applications on songs probably represent users’ judgments towards the songs, rather than descriptions of the moods conveyed by the songs. Therefore, such tags are noise and should be eliminated. Another linguistic resource, General Inquirer (Stone, 1966), provides a list of judgmental terms. General Inquirer is a lexicon comprised of 11,788 words organized in 182 psychological categories, two of which are about 33
“evaluation” containing 492 words implying judgment and evaluation. In this study, these 492 words were subtracted from the terms in WordNet-Affect, which resulted in 1,384 terms. 3.1.2 Obtaining Mood-related Social Tags Many of the mood-related terms obtained so far may not be used by music taggers, and thus cannot be used for mood categories that aim to reflect the music listening reality. Hence, the next step is to obtain mood-related social tags. Many music websites provide social tagging functions, and a few of them have gained significant popularity among the general public and have accumulated a large number of social tags. Last.fm is one of the most popular tagging sites for Western music 7 . With 30 million users every month, it provides a rich resource of studying how people tag music. According to Lamere (2008), last.fm has over 40 million tags as of 2008. Over half of them are on tracks and 5% of the tags are directly related to mood. In this research, it is the author’s intention to only use tags published on one website. This is because each website has its own user community, and combining tags across websites would lose the coherence and identity of the user community. The author queried last.fm through its API 8 with the 1,384 mood-related terms identified so far, and 611 of them had been used as tags by last.fm users as of June 2009. To untangle the “long-tail” problem mentioned above, tags that were used less than 100 times were eliminated. 236 terms/tags remained after this step. 7 Social Media Statistics http://socialmediastatistics.wikidot.com/lastfm Retrieved on July 22, 2008. 8 Accessible at http://www.last.fm/api 34
Page 1 and 2: IMPROVING MUSIC MOOD CLASSIFICATION
Page 3 and 4: concatenation and late fusion (line
Page 5 and 6: ACKNOWLEDGMENTS I would have never
Page 7 and 8: TABLE OF CONTENTS LIST OF FIGURES .
Page 9 and 10: 7.2.2 Best Hybrid Method ..........
Page 11 and 12: LIST OF TABLES Table 3.1 Mood categ
Page 13 and 14: music itself and creating the socia
Page 15 and 16: the ultimate judge. Thus ground tru
Page 17 and 18: The advantages have attracted resea
Page 19 and 20: Research Question 2: Which type(s)
Page 21 and 22: There are two popular approaches in
Page 23 and 24: 1.4.1 Contributions to Methodology
Page 25 and 26: experimental datasets in music mood
Page 27 and 28: 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: 2.5 SUMMARY This chapter provided a
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 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
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6.4.3 Analysis of Text Stylistic Fe
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Figure 6.1 Distributions of “!”
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stylistic features appeared to be a
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Support Vector Machines (SVM) as it
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then compared to the feature concat
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According to the average accuracies
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systems and the audio-only system w
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Table 7.4 Accuracies of lyric and a
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7.4.2 Top-Ranked Features Based on
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Again, these top-ranked features se
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However, some might argue that word
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Figure 8.1 shows a general trend th
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There are other interesting observa
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CHAPTER 9: CONCLUSIONS AND FUTURE R
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different strengths. Lyric-based sy
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close relationship with music mood.
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available, it is interesting to exp
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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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