Data mining and natural language processing researchers have been working on sentiment analysis for the past decade. Using deep neural networks (DNNs) for sentiment analysis has recently shown promising results. A technique of studying people’s attitudes through emotional sentiment analysis of data generated from various sources such as Twitter, social media reviews, etc. and classifying emotions based on the given data is related to text data generation. Therefore, the proposed study proposes a well-known deep learning technique for facet-based emotional mood classification using text data that can handle a large amount of content. Text data pre-processing uses stemming, segmentation, tokenization, case folding, and removal of stop words, nulls, and special characters. After data pre-processing, three word embedding approaches such as Assimilated N-gram Approach (ANA), Boosted Term Frequency Inverse Document Frequency (BT-IDF) and Enhanced Two-Way Encoder Representation from Transformers (E-BERT) are used to extract relevant features. The extracted features from the three different approaches are concatenated using the Feature Fusion Approach (FFA). The optimal features are selected using the Intensified Hunger Games Search Optimization (I-HGSO) algorithm. Finally, aspect-based sentiment analysis is performed using the Senti-BILSTM (Deep Aspect-EMO SentiNet) autoencoder based on the Hybrid Emotional Aspect Capsule autoencoder. The experiment was built on the yelp reviews dataset, IDMB movie review dataset, Amazon reviews dataset and the Twitter sentiment dataset. A statistical evaluation and comparison of the experimental results are conducted with respect to the accuracy, precision, specificity, the f1-score, recall, and sensitivity. There is a 99.26% accuracy value in the Yelp reviews dataset, a 99.46% accuracy value in the IMDB movie reviews dataset, a 99.26% accuracy value in the Amazon reviews dataset and a 99.93% accuracy value in the Twitter sentiment dataset.

过去十年来，数据挖掘和自然语言处理研究人员一直致力于情感分析。使用深度神经网络 (DNN) 进行情感分析最近显示出有希望的结果。通过对从诸如Twitter、社交媒体评论等各种来源生成的数据进行情感情感分析来研究人们的态度并基于给定数据对情感进行分类的技术与文本数据生成相关。因此，本研究提出了一种众所周知的深度学习技术，使用可以处理大量内容的文本数据进行基于方面的情绪情绪分类。文本数据预处理使用词干、分段、标记化、大小写折叠以及删除停用词、空值和特殊字符。数据预处理后，使用同化 N 元语法法 (ANA)、提升词频逆文档频率 (BT-IDF) 和增强型双向编码器表示法 (E-BERT) 等三种词嵌入方法进行提取相关功能。使用特征融合方法 (FFA) 连接从三种不同方法提取的特征。使用强化饥饿游戏搜索优化 (I-HGSO) 算法选择最佳特征。最后，使用基于混合情感方面胶囊自动编码器的 Senti-BILSTM（Deep Aspect-EMO SentiNet）自动编码器执行基于方面的情感分析。该实验建立在 yelp 评论数据集、IDMB 电影评论数据集、亚马逊评论数据集和 Twitter 情绪数据集上。对实验结果的准确性、精密度、特异性、f1 分数、召回率和灵敏度进行统计评估和比较。 Yelp 评论数据集的准确度为 99.26%，IMDB 电影评论数据集的准确度为 99.46%，Amazon 评论数据集的准确度为 99.26%，Twitter 情绪数据集的准确度为 99.93%。