## 4 Experimental Results and Discussion
In order to demonstrate the generalization ability of the proposed model, we conducted the second experiment using the E-commerce service review dataset (ECSR) ([[https://github.com/renjuniciang/Text-Classification](https://github.com/renjuniciang/Text-Classification)]([https://github.com/renjuniciang/Text-Classification](https://github.com/renjuniciang/Text-Classification))). This dataset consists mainly of the review comments for TV products and distribution services collected from several E-commerce websites. In this dataset, the average length of review comments was 72 words. Each review was given a sentiment tag: _positive or negative_. The data contained a total of 4212 reviews, in which 1883 were positive and 2329 were negative. We performed 10-fold cross validation to examine the performance. Here, we set the dropout rate as 0.25 and the batch size as 32, and we used the Adam optimizer with a learning rate of 0.01. Our proposed BiLSTM-based sentiment classification approach was implemented using Keras. We used precision, recall, and F\({}_{1}\)-measure for our evaluations [32]. Furthermore, we calculated the macro average of these metrics for the overall comparison. Precisely, letting \(C_{i}\) be the corpus in our studies, we calculated precision, recall, and F\({}_{1}\)-measure \(P(C_{i})\), \(R(C_{i})\), \(F_{1}(C_{i})\), and micro-average \(F^{\mu}\), as in Equations (4)-(7). \[P(C_{i})=\frac{TP(C_{i})}{TP(C_{i})+FP(C_{i})} \tag{4}\]  
\[R(C_{i})=\frac{TP(C_{i})}{TP(C_{i})+FN(C_{i})} \tag{5}\]  
\[F_{1}(C_{i})=\frac{2\times P(C_{i})\times R(C_{i})}{P(C_{i})+R(C_{i})} \tag{6}\]  
\[F^{\mu}(C_{i})=\frac{\sum_{i=1}^{n}2\times P(C_{i})\times R(C_{i})}{\sum_{i=1} ^{n}P(C_{i})+R(C_{i})} \tag{7}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Valence**} & \multicolumn{2}{c}{**Arousal**} \\ \cline{2-5}  & **MAE** & **PCC** & **MAE** & **PCC** \\ \hline
**THU_NGN** & **0.509** & **0.908** & 0.864 & 0.686 \\
**Our method** & 0.543 & 0.887 & **0.855** & **0.689** \\
**AL_I_NLP** & 0.545 & 0.892 & 0.857 & 0.678 \\
**CRIP** & 0.602 & 0.858 & 0.949 & 0.576 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Performance comparison of our method and the top-ranking teams in the Valence and Arousal value prediction task in the Dimensional Sentiment Analysis for Chinese Phrases (DSA_P) Competition, 2017.  
where \(TP(C_{i})\) indicates the quantity of correct positive cases and \(FP(C_{i})\) denotes the number of false positives (namely, negative cases that are wrongly classified as positives). Analogously, \(TN(C_{i})\) and \(FN(C_{i})\) indicate the number of true negatives and false negatives, respectively. To systematically assess the relative effectiveness of the compared methods, the F\({}_{1}\) value is also used. Next, we evaluated the performance of the embeddings to demonstrate the effectiveness of our novel text representation method. Table 2 shows the gain in performance after applying LLR and VA, denoted as EmBERT+LLR + BiLSTM_Att and EmBERT+LLR+VA + BiLSTM_Att, respectively. To provide an all-inclusive performance evaluation, we compared our method to the state-of-the-art system (denoted as NNLM) of the NLPCC 2014 dataset. As shown in this table, the EmBERT +BiLSTM_Att can achieve about 74% and 87% F\({}_{1}\)-score on NLPCC 2014 and ECSR datasets, respectively. By using LLR features, we further improved the system performance because it successfully discriminates words that are highly correlated with a certain emotion, thereby boosting the BiLSTM's ability to find representative lexicons of sentiment. Moreover, the VA features improve the F\({}_{1}\) score significantly. It indicates that when the model considers both valence value as the polarity and arousal value as the strength of sentiment, it can greatly improve the effectiveness of sentiment analysis. Notably, our method outperforms the comparison in each and every category.