Name
Title | ||
|---|---|---|
Identifying Accuracy of Social Tags by Using Clustering Representations of Song Lyrics |
Abstract | ||
|---|---|---|
Social tags have been acknowledged as a highly useful resource in retrieving music by moods or topics. However, since social tags are open for labeling, some social tags are inaccurate. In this paper, we present a new framework to identify accurate social tags of songs. In our framework, we first clean and filter music tags. Then we apply an improved hierarchical clustering algorithm to group the tags to build a tag category. Based on the category, we classify music songs using lyrics. In order to extend the semantic information of lyrics, we apply CLOPE to cluster lyrics and use the centroid of the corresponding cluster to represent the lyrics. Based on the Na"ive Bayes method, the probability of assigning lyrics to particular class is predicted. The classification result is then used to determine whether a social tag is accurate. The experimental results show that the proposed framework is effective and encouraging. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICMLA.2012.107 | ICMLA (1) |
Keywords | Field | DocType |
tag grouping,corresponding cluster,mood,pattern clustering,text classification,proposed framework,bayes methods,music,lyrics representation,lyrics,pattern classification,retrieving music,song lyrics,music tag cleaning,clope,social tag,social tags,accurate social tag,clustering representations,music song classification,music tag,lyrics semantic information,naive bayes method,topic,new framework,hierarchical clustering algorithm,identifying accuracy,labeling,text analysis,social tag accuracy identification,music retrieval,tag category,music tag filtering,lyrics clustering,content-based retrieval,cluster lyric,music song,probability | Hierarchical clustering,Pattern recognition,Pattern clustering,Computer science,Semantic information,Artificial intelligence,Lyrics,Social tags,Cluster analysis,Centroid,Bayes' theorem | Conference |
Volume | ISBN | Citations |
1 | 978-1-4673-4651-1 | 1 |
PageRank | References | Authors |
0.37 | 2 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yajie Hu | 1 | 68 | 4.59 |
| Mitsunori Ogihara | 2 | 3135 | 257.04 |