As the number of users getting acquainted with the Internet is escalating rapidly, there is more user-generated content on the web. Comprehending hidden opinions, sentiments, and emotions in emails, tweets, reviews, and comments is a challenge and equally crucial for social media monitoring, brand monitoring, customer services, and market research. Sentiment analysis determines the emotional tone behind a series of words may essentially be used to understand the attitude, opinions, and emotions of users. We propose a novel long short-term memory (LSTM)–convolutional neural networks (CNN)–grid search-based deep neural network model for sentiment analysis. The study considers baseline algorithms like convolutional neural networks, K-nearest neighbor, LSTM, neural networks, LSTM–CNN, and CNN–LSTM which have been evaluated using accuracy, precision, sensitivity, specificity, and F-1 score, on multiple datasets. Our results show that the proposed model based on hyperparameter optimization outperforms other baseline models with an overall accuracy greater than 96%.

随着熟悉 Internet 的用户数量迅速增加，网络上出现了更多用户生成的内容。理解电子邮件、推文、评论和评论中隐藏的观点、情绪和情绪是一项挑战，对于社交媒体监控、品牌监控、客户服务和市场研究同样重要。情感分析决定了一系列词语背后的情感基调，本质上可以用来理解用户的态度、观点和情绪。我们提出了一种新颖的长短期记忆（LSTM）-卷积神经网络（CNN）-基于网格搜索的深度神经网络模型，用于情感分析。该研究考虑了基线算法，如卷积神经网络、K-最近邻、LSTM、神经网络、LSTM-CNN 和 CNN-LSTM，已在多个数据集上使用准确性、精度、灵敏度、特异性和 F-1 分数进行了评估。我们的结果表明，所提出的基于超参数优化的模型优于其他基线模型，总体准确率大于 96%。