Decoding Emotions in Text Using GloVe Embeddings

The process of identifying human emotions in text is a relatively nascent research domain. While multiple approaches such as observing facial expressions from video, spoken expressions from audio, written expressions from the text, and physiology measured by wearable devices have been taken in the past, multi-modal approaches have shown promising results too. In this paper, we utilize the content of text to determine the emotions expressed therein by the writer. Semantic embeddings are derived from the text through the means of GloVe - "an unsupervised learning algorithm for obtaining vector representations for words" [1], which was chosen because of its ability to incorporate global statistics and not relying on the local statistics or local contextual information of words. The embeddings this obtained were passed through LSTM - "an extension of recurrent neural networks (RNNs) that is capable of handling long term dependencies" [2]. Our model was able to attain an overall F1 score of 0.93. While the model recognized joy and sadness better than other labels, it found surprise harder to detect. On emotion recognition tasks, our approach of using GloVe Embedding has not been extensively studied in the past.