Month: March 2018

Abstract: This paper discusses the approach taken by the UWaterloo staff to arrive at a solution for the Fine-Grained Sentiment Analysis drawback posed by Task 5 of SemEval 2017. The paper describes the doc vectorization and sentiment score prediction methods used, in addition to the design and implementation decisions taken whereas constructing the system for this job. The system uses textual content vectorization fashions, corresponding to N-gram, TF-IDF and paragraph embeddings, coupled with regression model variants to predict the sentiment scores. Amongst the strategies examined, unigrams and bigrams coupled with simple linear regression obtained one of the best baseline accuracy. The paper also explores data augmentation methods to complement the training dataset. This system was designed for Subtask 2 (News Statements and Headlines).

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