  Nowadays, listening music has been and will always be an indispensable part
of our daily life. In recent years, sentiment analysis of music has been widely
used in the information retrieval systems, personalized recommendation systems
and so on. Due to the development of deep learning, this paper commits to find
an effective approach for mood tagging of Chinese song lyrics. To achieve this
goal, both machine-learning and deep-learning models have been studied and
compared. Eventually, a CNN-based model with pre-trained word embedding has
been demonstrated to effectively extract the distribution of emotional features
of Chinese lyrics, with at least 15 percentage points higher than traditional
machine-learning methods (i.e. TF-IDF+SVM and LIWC+SVM), and 7 percentage
points higher than other deep-learning models (i.e. RNN, LSTM). In this paper,
more than 160,000 lyrics corpus has been leveraged for pre-training word
embedding for mood tagging boost.
