improving music mood classification using lyrics, audio and social tags

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3.1.3 Cleaning up Social Tags by Human Experts Human expertise is applied as a final step to ensure the quality of the mood-related term list. This research consulted two human experts who were respected MIR researchers with a music background and native English speakers. In this dissertation research where human experts were consulted with regard to music mood terms, the two experts worked together at the same place and time. They manually examined the terms and discussed discrepant opinions with each other until they reached the same decisions. In this way, all terms were considered by both experts and all decisions were made by the best judgments of both experts. In this particular task of cleaning up non-mood tags, the experts examined the remaining 236 terms. They first identified and removed tags with music meanings that did not involve an affective aspect (e.g., “trance” and “beat”). Then, they removed words with ambiguous meanings. For example, “chill” can mean “to calm down” or “depressing,” but social tags do not provide enough context to disambiguate the term. Finally, they also identified and removed additional evaluation words that were not included in General Inquirer, such as “fascinating” and “dazzling.” After this step, there remained 136 mood-related terms. 3.1.4 Grouping Mood-related Social Tags As a means of solving the synonym problem of social tags, the mood-related tags are organized into groups such that synonyms are merged together into one group. Tags in each resultant group then collectively define a mood category. This step again uses WordNet-Affect. WordNet is a natural resource for identifying synonyms, because it organizes words into synsets. Words in the same synset are synonyms from a linguistic point of view. Moreover, WordNet- Affect also links each non-noun synset (verb, adjective and adverb) with the noun synset from 35
which it is derived. For instance, the synset of “joyful” is marked as derived from the synset of “joy.” Both synsets of “joyful” and “joy” represent the same kind of mood and should be merged into the same category. Hence, among the 136 mood-related tags, those appearing in and being derived from the same synset in WordNet-Affect were merged into one group. Finally, human experts were again consulted to modify the grouping of tags when they saw the need for splitting or further merging some groups. Each of the resultant groups of social tags is taken as one mood category that is collectively defined by all the tags in the group. The categories and the comparisons to psychological models are reported in the next sections. 3.2 MOOD CATEGORIES Using the method described in the last section, a set of 36 mood categories consisting of 136 social tags were identified from the most popular mood-related social tags published on last.fm. Using the linguistic resources allows this process to proceed quickly and minimizes the workload of the human experts. Hence the experts can focus on the few tasks that need human expertise most and ensure the quality of their work. Table 3.1 presents the categories and the tags contained in each category. Table 3.1 Mood categories derived from last.fm tags Categories calm, calm down, calming, calmness, comfort, comforting, cool down, quiet, relaxation, serene, serenity, soothe, soothing, still, tranquil, tranquility Number of tags gloomy, blue, dark, depress, depressed, depressing, depression, depressive, gloom 9 mournful, grief, heartache, heartbreak, heartbreaking, mourning, regret, sorrow, sorrowful gleeful, euphoria, euphoric, high spirits, joy, joyful, joyous, uplift 8 cheerful, cheer up, cheer, cheery, festive, jolly, merry, sunny 8 16 9 36
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 and 44: 2.5 SUMMARY This chapter provided a
Page 45: “evaluation” containing 492 wor
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
Page 95 and 96: 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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