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Curriculum Learning (CL) is the idea that learning on a training setsequenced or ordered in a manner where samples range from easy to difficult,results in an increment in performance over otherwise random ordering. The ideaparallels cognitive science's theory of how human brains learn, and thatlearning a difficult task can be made easier by phrasing it as a sequence ofeasy to difficult tasks. This idea has gained a lot of traction in machinelearning and image processing for a while and recently in Natural LanguageProcessing (NLP). In this paper, we apply the ideas of curriculum learning,driven by SentiWordNet in a sentiment analysis setting. In this setting, givena text segment, our aim is to extract its sentiment or polarity. SentiWordNetis a lexical resource with sentiment polarity annotations. By comparingperformance with other curriculum strategies and with no curriculum, theeffectiveness of the proposed strategy is presented. Convolutional, Recurrence,and Attention-based architectures are employed to assess this improvement. Themodels are evaluated on a standard sentiment dataset, Stanford SentimentTreebank.

A SentiWordNet Strategy for Curriculum Learning in Sentiment Analysis