With the presence of a massive amount of digitally recorded data, an automated computation can be preferable over the manual approach to evaluate sentiments within given textual fragments. Artificial neural network (ANN) is preferred for sentiment analysis (SA) because of its learning ability and adaptive nature towards diverse data. Handling negation in SA is a challenging task, and to address the same, we propose a specific order of preprocessing (PPR) steps to enhance the performance of SA using ANN. Typically, ANN weights are randomly initialized (R-ANN), which may not give the desired performance. As a potential solution, we propose a novel approach named Matching features with output label based Advanced Technique (MAT) to initialize the ANN weights (MAT-ANN). Simulation results conclude the superiority of the proposed approach PPR+MAT-ANN compared to the existing approach EPR+R-ANN i.e., integrating existing preprocessing (EPR) steps with R-ANN. Moreover, PPR+MAT-ANN architecture is significantly simpler than the existing deep learning-based approach named the NeuroSent tool and gives better performance when evaluated upon the Dranziera protocol.

中文翻译：

---

使用增强的预处理技术和人工神经网络提高情感分析的性能

随着大量数字记录数据的存在，自动计算可能比手动方法更可取，以评估给定文本片段中的情绪。人工神经网络 (ANN) 因其学习能力和对不同数据的适应性而成为情感分析 (SA) 的首选。在 SA 中处理否定是一项具有挑战性的任务，为了解决这个问题，我们提出了一个特定的预处理顺序 (PPR) 步骤，以使用 ANN 增强 SA 的性能。通常，ANN 权重是随机初始化的 (R-ANN)，这可能无法提供所需的性能。作为一种潜在的解决方案，我们提出了一种名为匹配特征与基于输出标签的高级技术 (MAT) 的新方法来初始化 ANN 权重 (MAT-ANN)。仿真结果得出结论，与现有方法 EPR+R-ANN 相比，所提出的方法 PPR+MAT-ANN 具有优越性，即将现有预处理 (EPR) 步骤与 R-ANN 相结合。此外，PPR+MAT-ANN 架构比现有的名为 NeuroSent 工具的基于深度学习的方法简单得多，并且在根据 Dranziera 协议进行评估时提供更好的性能。