Sentiment understanding has been a long-term goal of AI in the past decades.
This paper deals with sentence-level sentiment classification. Though a variety
of neural network models have been proposed very recently, however, previous
models either depend on expensive phrase-level annotation, whose performance
drops substantially when trained with only sentence-level annotation; or do not
fully employ linguistic resources (e.g., sentiment lexicons, negation words,
intensity words), thus not being able to produce linguistically coherent
representations. In this paper, we propose simple models trained with
sentence-level annotation, but also attempt to generating linguistically
coherent representations by employing regularizers that model the linguistic
role of sentiment lexicons, negation words, and intensity words. Results show
that our models are effective to capture the sentiment shifting effect of
sentiment, negation, and intensity words, while still obtain competitive
results without sacrificing the models' simplicity.

本研究提出了使用句子级别注释训练的简单模型，同时采用正则化方法，以模拟情感词汇、否定词和强度词汇的语言学角色，从而生成语言学相关的表示形式，并且能够捕捉情感、否定和强度词汇的情感转移效应，同时在不损失模型简洁性的前提下获得竞争性的结果。