AN EMPIRICAL STUDY ON THE EFFECT OF CAREER GOAL SETTING

Abstract

Negation words, such as no and not, play a fundamental role inmodifying sentiment of textual expressions. We will refer to a negation word as the negator and the text span within the scope of the negator as the argument. Commonly used heuristics to estimate the sentiment of negated expres- sions rely simply on the sentiment of ar- gument (and not on the negator or the ar- gument itself). We use a sentiment tree- bank to show that these existing heuristics are poor estimators of sentiment. We then modify these heuristics to be dependent on the negators and show that this improves prediction. Next, we evaluate a recently proposed composition model (Socher et al., 2013) that relies on both the negator and the argument. This model learns the syntax and semantics of the negator’s ar- gument with a recursive neural network. We show that this approach performs bet- ter than those mentioned above. In ad- dition, we explicitly incorporate the prior sentiment of the argument and observe that this information can help reduce fitting er- rors. 1