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- W2045558466 abstract "Song sentiment analysis attracts much attention in research areas such as acoustic signal processing (ASP) and natural language processing (NLP). The text-based efforts in the NLP community are found to be interesting. As a popular text-based solution, the lyric-based sentiment classification approach adopts the vector space model (VSM) to represent lyric text and assigns songs sentiment labels such as light-hearted and heavy-hearted. Four problems render the term-based VSM model ineffective. Firstly, many words within song lyrics contribute little to expressing sentiment, but they are equally considered as term features. Secondly, nouns and verbs being used to express sentiment are ambiguously used in natural language text. But the ambiguity is not addressed. Thirdly, negations and modifiers accompanying the sentiment keywords make particular contributions to sentiment expressing, but the contributions are not reflected. Lastly, song lyric is usually very short and data sparseness problem is serious. To address these problems, the sentiment vector space model (s-VSM) is proposed in this paper to represent song lyric document based on sentiment unit. With the s-VSM model, the support vector machines (SVM) classification algorithm is applied to perform song sentiment analysis. Experimental results show that lyric is helpful to song sentiment analysis and the s-VSM model outperforms the VSM model significantly in performing the task of song sentiment analysis." @default.
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- W2045558466 date "2008-12-01" @default.
- W2045558466 modified "2023-09-30" @default.
- W2045558466 title "Sentiment Vector Space Model for Lyric-Based Song Sentiment Classification" @default.
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- W2045558466 doi "https://doi.org/10.1142/s1793840608001950" @default.
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