## 6 Discussion  
The paper proposes an enhanced LSTM framework (DPG-LSTM) for sentiment analysis in social media text based on DP and GCN. Due to the complexities of data in social media, there is a large diversity of semantic information, as well as specialized syntactic information. For DPG-LSTM, we employ the BiLSTM as the semantic information analysis module. Although LSTM overcomes the problem of RNN gradient explosion and captures semantic dependencies over greater distances, LSTM can better capture the proximity semantics in social media from the reverse. Because the syntactic relations derived via DP are essentially non-Euclidean data structures, conventional neural networks based on Euclidean data structures are unable to parse the syntactic information adequately.  
Figure 6: Results at different text lengths.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**Visualization of Sentence**} & **Prediction** & **Label** \\ \hline \multirow{5}{*}{BiLSTM-GA} & I have when I have to call and wake people up & negative & negative \\ \cline{2-6}  & I low write but I have little editing and proofing & positive & negative \\ \cline{2-6}  & What a good and if just hanging out and not really drinking & positive & positive \\ \cline{2-6}  & My exams are about to end finally & I have not just one exam left, I was so & negative & positive \\ \cline{2-6}  & and of them. & & negative & negative \\ \hline \multirow{5}{*}{DPG-LSTM} & I love write but I have little editing and proofing & negative & negative \\ \cline{2-6}  & What a good and if just hanging out and not & drinking & positive & positive \\ \cline{1-1} \cline{2-6}  & My exams are about to end finally & I have not been seen & I was & positive & positive \\ \cline{1-1} \cline{2-6}  & and of them. & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Visualization Case.  
Such non-Euclidean data structures are properly represented by graphs, and the syntactic properties of the nodes are well constructed using the GCN, which can analyze graphs directly. The current mainstream attention mechanism focuses on exploring the effect of each word in a sentence on the sentence's semantic level, assigning attention weights to each word, and capturing the relevant components of the sentence semantics. Our suggested DPG-Attention, which is based on DP theory, captures not only the significant components of phrase semantics but also those of sentence syntax. The combination of these methods makes it possible to improve the classification ability of DPG-LSTM. Experiments have shown that GCN and DPG-Attention have a significant influence on DPG-LSTM performance. DPG-Attention, in particular, has a larger influence on categorization accuracy than GCN. The experiments also reveal that the length of the text has an impact on DPG-LSTM performance.  
Previous studies simply combined semantic and syntactic data in social media, with the majority of them employing step-by-step feature extraction or feature concatenation techniques. Compared with earlier research, the study we propose varies in two main aspects. First, we present a dual-channel neural network that can collect both semantic and syntactic information in a social media text. Second, the upgraded DPG-Attention is adopted with DP to completely incorporate the semantic and syntactic information. The sentence feature is augmented with additional syntactic information by the DPG-Attention to produce superior sentiment analysis results.  
In future work, there are still two points that can be improved. The existence or absence of dependence relations is used to generate the dependency graph, although this dependency graph does not adequately capture the whole sentence's syntactic information. Furthermore, our present studies are focused on English tweet data, but social media is made up of a variety of national languages.