## 2 Model
### Feature-Enhanced Word Embedding Module  
The first step is to pre-process the input sentence and sentiment resource words. In order to transfer each word in the sentence to a real-value vector, we applied the pre-trained GloVe [18] method in the word embedding layer. Let \(L\in R^{|V|\times d}\) be the embedding lookup table generated by GloVe, where \(d\) is the dimension of word vector and \(|V|\) is the vocabulary size. Suppose the input sentence consists of \(n\) words and the sentiment resource sequence consists of \(m\) words. The input sentence retrieves the word vectors from \(L\) and gets a list of vectors \([w_{1},\ w_{2},\ \cdots,\ w_{n}]\) where \(w_{i}\in R^{d}\) is the word vector of the \(i^{th}\) word. Similarly, the sentiment resource sequence can retrieve the word vectors from \(L\) and form a list of vectors \(\left[w_{1}^{s},\ w_{2}^{s},\ \cdots,\ w_{m}^{s}\right]\). In this way, we can get the matrix \(W^{c}=[w_{1},\ w_{2},\ \cdots,\ w_{n}]\in R^{n\times d}\) for context words and the matrix \(W^{s}=\left[w_{1}^{s},\ w_{2}^{s},\ \cdots,\ w_{m}^{s}\right]\in R^{n \times d}\) for sentiment resource words. After obtaining the general matrix of the word vector, we propose the novel sentiment attention mechanism to help highlight the vital sentiment-resource-relevant context words for generating the sentence representation with enhanced sentiment features. Specifically, we leverage sentiment words in the sentiment lexicon as sentiment resource words and integrate them with the attention mechanism to emphasize more important information related to the sentiment polarity. The sentiment lexicon collects sentiment resource words from both [PERSON] and [PERSON] [19] and [PERSON] et al. [20], including 10,899 sentiment resource words in total. Then, the attention mechanism uses sentiment resource words as an attention source attending to the context words to learn the feature-enhanced word embedding. In the following, we will describe the sentiment attention mechanism in detail. Figure 1: The architecture of the proposed sentiment-feature-enhanced deep neural network (SDNN) model. First, inspired by the fact that the sentiment words can largely guide the sentiment polarity in the context of a sentence, we plan to design the word-level relationship between the sentiment words and the context words. For example, in the sentence \"This movie is so wasteful of talent, it is truly disgusting\", composed of the sentiment words (i.e., \"wasteful\" and \"disgusting\") and the context words (i.e., all words in this sentence except the sentiment words), \"wasteful\" and \"disgusting\" play a key role in directing the sentiment polarity of this sentence. Mathematically, we adopt the dot product operation between the context words and the sentiment words to form a correlation matrix. The specific calculation method is as follows:  
\[M^{s}=W^{c}\circ\left(W^{s}\right)^{T} \tag{1}\]  
where \(\circ\) denotes the dot product operation and \(M^{s}\in R^{n\times m}\) represents the relevance matrix between the context words and the sentiment words. Then, we define the context-word-relevant sentiment word representation matrix \(X^{s}\) generated by the dot product operation between the context words \(W^{c}\) and the correlation matrix \(M^{s}\):  
\[X^{s}=\left(W^{c}\right)^{T}\circ M^{s} \tag{2}\]  
where \(X^{s}\in R^{d\times m}\) represents the sentiment word representation matrix related to the context words. Similarly, we can compute the sentiment-word-relevant context word representation matrix \(X^{c}\) by the dot product between the sentiment words \(W^{s}\) and the correlation matrix \(M^{s}\):  
\[X^{c}=\left(M^{s}\circ W^{s}\right)^{T} \tag{3}\]  
where \(X^{c}\in R^{d\times m}\) represents the context word representation matrix related to the sentiment words.