  Emotion recognition from a given music track has heavily relied on acoustic
features, social tags, and metadata but is seldom focused on lyrics. There are
no datasets of Indian language songs that contain both valence and arousal
manual ratings of lyrics. We present a new manually annotated dataset of Telugu
songs' lyrics collected from Spotify with valence and arousal annotated on a
discrete scale. A fairly high inter-annotator agreement was observed for both
valence and arousal. Subsequently, we create two music emotion recognition
models by using two classification techniques to identify valence, arousal and
respective emotion quadrant from lyrics. Support vector machine (SVM) with term
frequency-inverse document frequency (TF-IDF) features and fine-tuning the
pre-trained XLMRoBERTa (XLM-R) model were used for valence, arousal and
quadrant classification tasks. Fine-tuned XLMRoBERTa performs better than the
SVM by improving macro-averaged F1-scores of 54.69%, 67.61%, 34.13% to 77.90%,
80.71% and 58.33% for valence, arousal and quadrant classifications,
respectively, on 10-fold cross-validation. In addition, we compare our lyrics
annotations with Spotify's annotations of valence and energy (same as arousal),
which are based on entire music tracks. The implications of our findings are
discussed. Finally, we make the dataset publicly available with lyrics,
annotations and Spotify IDs.
