# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 3 The Proposed Method and Its Applications on Downstream Tasks
### Applications of the Sentiment-Enhanced Word Embedding Method on Downstream Tasks  
Since the S-EWE method needs to work with a sentiment lexicon, we need a robust sentiment lexicon. We selected the extended version of Affective Norms of English Words (E-ANEW) [50] and the Subjectivity Clue Lexicon [51] as our base sentiment lexicons, for they are well-known English sentiment lexicons. For the E-ANEW [50], we define sentiment words with sentiment intensity between \([1,5)\) as negative, sentiment words with sentiment intensity of 5 as neutral, and sentiment words with sentiment intensity between \((5,9]\) as positive. For the Subjectivity Clue Lexicon [51], we directly extract the sentiment orientation (positive, neutral, negative). To improve the robustness of these two lexicons, we adopted the following four rules to integrate them: (1) if a word appears in only one lexicon, then the word is directly added into the fused lexicon; (2) if a word has the same sentiment orientation in the two lexicons, then the word is directly added into the fused lexicon; (3) if the sentiment orientation of a word is neutral in one lexicon and the sentiment orientation is not neutral in another lexicon, then the word is assigned with non-neutral sentiment orientation and added into the fused lexicon; and (4) if a word has opposite sentiment orientations (positive and negative) in the two lexicons, then the word is discarded and does not belong to the fused lexicon. After finishing the above four rules, we can obtain _the fused lexicon_. Table 1 shows the details of the E-ANEW, Subjectivity Clue Lexicon, and fused lexicon, respectively. The words in the sentiment lexicon may not all appear in the corpus in most cases. Meanwhile, the S-EWE method will waste some storage space to train the words in the lexicon and may not obtain good sentiment-enhanced word embeddings. Inspired by sentiment-aware word embeddings (SAWE) [14], we extract all words from the corpus and \begin{table}
\begin{tabular}{l c c c} \hline \hline  & **E-ANEW** & **Subjectivity Clue Lexicon** & **Fused Lexicon** \\ \hline Total number of words & 13,915 & 6869 & 17,293 \\ The number of positive words & 7761 & 2296 & 8672 \\ The number of negative words & 5945 & 4149 & 8216 \\ The number of neutral words & 209 & 424 & 405 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of the sentiment lexicons. build a _final lexicon_ by the following two rules. (1) If a word appears in both the corpus and the fused lexicon, we directly add it and its corresponding sentiment orientation in the fused lexicon to the final lexicon. (2) If a word appears in the corpus but not in the fused lexicon, we add it to the final lexicon with the neutral sentiment. Based on the fused and final lexicons, the sentiment-enhanced word embeddings are applied to downstream tasks for sentiment analysis, as shown in Figure 4. When the sentiment-enhanced word embeddings are trained by the S-EWE method on the fused lexicon (respectively, the final lexicon), we denote the well-trained embeddings by S-EWE\({}_{\text{f}}\) (respectively, S-EWE\({}_{\text{c}}\)). Figure 4 shows the applications of the sentiment-enhanced word embeddings trained by the S-EWE method based on the final and fused lexicons for downstream tasks. Figure 3(a) shows how we apply S-EWE\({}_{\text{f}}\) to downstream tasks, and Figure 3(b) represents how we apply S-EWE\({}_{\text{c}}\) to downstream tasks. When the fused lexicon does not contain some words in the corpus, we adopt the following two methods to get the embedding layer of the downstream sentiment analysis models (Figure 3(a)). (1) If a word is in the fused lexicon, we input the well-trained sentiment-enhanced word embedding by the S-EWE method in the fused lexicon as the word embedding with sentiment information into the embedding layer of the downstream tasks. (2) If a word is not in the fused lexicon, we use the original word embedding as the word embedding with sentiment information into the embedding layer of the downstream tasks.