  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.
