基于情感向量空间模型的歌曲情感标签预测模型

音乐的情感标签预测对音乐的情感分析有着重要的意义。该文提出了一种基于情感向量空间模型的歌曲情感标签预测算法,首先,提取歌词中的情感特征词构建情感空间向量模型,然后利用SVM分类器对已知情感标签的音乐进行训练,通过分类技术找到与待预测歌曲情感主类一致的歌曲集合,最后,通过歌词的情感相似度计算找到最邻近的k首歌曲,将其标签推荐给待预测歌曲。实验发现本文提出的情感向量空间模型和“情感词—情感标签”共现的特征降维方法比传统的文本特征向量模型能够更好地提高歌曲情感分类准确率。同时,在分类基础上进行的情感标签预测方法可以有效地防止音乐“主类情感漂移”,比最近邻居方法达到更好的标签预测准确率。

Sentiment Vector Space Model Based Music Emotion Tag Prediction

Music emotion tag prediction algorithm plays an important role in music sentiment analysis. This paper presents a sentiment vector space model (s-VSM) based music emotion tag prediction algorithm. Firstly, we extract the emotion words to build the sentiment vector space model. Then, we use SVM classifier to generate training samples, and to get the collection which shares the same main emotion category with the predicted music. Finally, by finding the nearest k songs, we can get the emotion tag for recommendation. Experimental results show that s-VSM and the “emotional words-emotional label” co-occurrence based feature reduction method perform better than traditionally word-based vector space model in mood classification. Meanwhile, the emotion tag prediction based on the result of classification can effectively prevent the music “main mood drift”, thus achieving better tag predict accuracy than k-nearest neighbors method.
Key wordstag prediction; feature reduction; mood classification; sentiment vector space model