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This work presents a new and simple approach for fine-tuning pretrained wordembeddings for text classification tasks. In this approach, the class in whicha term appears, acts as an additional contextual variable during the finetuning process, and contributes to the final word vector for that term. As aresult, words that are used distinctively within a particular class, will bearvectors that are closer to each other in the embedding space and will be morediscriminative towards that class. To validate this novel approach, it wasapplied to three Arabic and two English datasets that have been previously usedfor text classification tasks such as sentiment analysis and emotion detection.In the vast majority of cases, the results obtained using the proposedapproach, improved considerably.

A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text Classification