# Research on Online Review Information Classification Based on Multimodal Deep Learning
## 5 Experiments and Analysis of Results
### Experimental Process and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Classification Results**} \\ \hline
**Serial Number** & **Comments** & **Taste** & **Price** & **Packaging** & **Service** & **Delivery Time** & **Hygiene** & **Environment** \\ \hline
1179 & Let’s not talk about the other flavors. It’s the diarrhea that’s real. I clearly ordered two orders of thin rice, can’t you see’? I’ll never buy it again. & 0 & 0 & 0 & \(-1\) & 0 & \(-1\) & 0 \\
2980 & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\
5678 & & & & & & & & & \\  & & & & & & & & \\  & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: _Cont._  
Figure 6: Bert BiGRU Softmax model. Online reviews encompass consumer appraisals and assessments of merchandise sold on digital platforms. These evaluations encapsulate diverse dimensions including user encounters, product attributes, and satisfaction with services rendered. They serve as invaluable points of reference for both manufacturers and platform administrators. By scrutinizing customer feedback embedded within these reviews, vendors and platform operators can glean insights into the reception and efficacy of their offerings in the market. Consequently, they can embark on a journey of perpetual refinement in terms of product conception, whilst optimizing service procedures to align with user requisites and anticipations. This endeavor ensures enhanced product excellence and the preservation of enduring customer rapport. Through meticulous examination of customer feedback derived from reviews, vendors and platform operators are equipped with the means to ascertain the performance of their merchandise within the marketplace. Simultaneously, this practice allows them to continuously refine their product designs and optimize service processes in accordance with user needs and expectations. This concerted effort not only enhances product quality but also fosters enduring customer relationships. The proposed model comprises several integral components: foremost, the utilization of the BERT model as a feature extractor facilitates the extraction of semantic representations from the input layer's textual comments, thereby capturing nuanced semantic relations between words and contextual information. Subsequently, the integration of the BiGRU model, augmented with an attention mechanism, assumes the role of a hidden layer, facilitating the acquisition of high-dimensional semantic coding encompassing attention probabilities at the input layer and textual sequences of contextual information. Finally, the Softmax classifier is employed to categorize all comments into sentiment polarity prediction and trend classification tasks, thus enabling the effective recognition and analysis of users' affective tendencies and emotional attitudes. Comprehensive experiments were conducted on a substantial review dataset, comparing the performance of the proposed Bert BiGRU Softmax model against other advanced models such as CNN BiGRU, BiGRU, and BiGRU Attention. The experimental findings unequivocally demonstrate that the Bert BiGRU Softmax model exhibits superior performance and accuracy, thereby effectively augmenting the precision of sentiment analysis pertaining to online product quality reviews. Furthermore, we delve into the integration of domain expertise and human experiential insights into the training process of the model, aiming to cater more effectively to diverse industries and contextual nuances. Looking ahead, there is ample room for further refinement and optimization of the model. This can be achieved by amalgamating multimodal data and leveraging a plethora of information sources to conduct extensive and profound investigations into sentiment analysis. Such endeavors hold promise for uncovering novel breakthroughs and innovations, ultimately enhancing the performance of the model in real-world scenarios. J.L. : wrote the manuscript. Y.S. : optimized it. Y.Z. : methodology. C.L. : collected part of the data.