  Sentiment analysis is a key component in various text mining applications.
Numerous sentiment classification techniques, including conventional and deep
learning-based methods, have been proposed in the literature. In most existing
methods, a high-quality training set is assumed to be given. Nevertheless,
constructing a high-quality training set that consists of highly accurate
labels is challenging in real applications. This difficulty stems from the fact
that text samples usually contain complex sentiment representations, and their
annotation is subjective. We address this challenge in this study by leveraging
a new labeling strategy and utilizing a two-level long short-term memory
network to construct a sentiment classifier. Lexical cues are useful for
sentiment analysis, and they have been utilized in conventional studies. For
example, polar and privative words play important roles in sentiment analysis.
A new encoding strategy, that is, $\rho$-hot encoding, is proposed to alleviate
the drawbacks of one-hot encoding and thus effectively incorporate useful
lexical cues. We compile three Chinese data sets on the basis of our label
strategy and proposed methodology. Experiments on the three data sets
demonstrate that the proposed method outperforms state-of-the-art algorithms.
