Comparison of Word Embeddings and Sentence Encodings as Generalized Representations for Crisis Tweet Classification Tasks

Many machine learning and natural language processing approaches, including supervised and domain adaptation algorithms, have been proposed and studied in the context of filtering crisis tweets. However, the application of these approaches in practice is still challenging due to the time-critical requirements of emergency response operations, and also to the diversity and unique characteristics of emergency events. To address this limitation, we explore the idea of building “generalized” classifiers for filtering crisis tweets, classifiers which can be pre-trained and ready to use in real-time, while they generalize well on tweets from future disasters. We propose to achieve this objective using a simple feature-based adaptation approach, where tweets are represented as dense numeric vectors of reduced dimensionality using either word embeddings or sentence encodings. Given that several types of word embeddings and sentence encodings exist, we compare tweet representations corresponding to different word embeddings and sentence encodings with the goal of understanding what embeddings/encodings are more suitable for use in crisis tweet classification tasks. Our experimental results on three crisis tweet classification tasks suggest that the tweet representations based on GloVe embeddings produce better results than the representations that use other embeddings, when employed with traditional supervised learning algorithms. Furthermore, the GloVe embeddings trained on crisis data produce better results on more specific crisis tweet classification tasks (e.g., tweets informative versus non-informative), while the GloVe embeddings pre-trained on a large collection of general tweets produce better results on more general classification tasks (tweets relevant or not relevant to a crisis).