With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.

中文翻译：

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混合 CNN-LSTM：使用定性用户生成内容进行消费者情绪分析的深度学习方法

随着信息和通信技术 (ICT) 的快速发展，社交媒体平台上的 Web 内容的可用性与日俱增。在线评论的情绪分析引起了学术界、政府和私营企业等各种组织的研究人员的关注。情感分析一直是机器学习（ML）和自然语言处理（NLP）领域的热门研究课题。目前，深度学习（DL）技术在情感分析中得到了很好的结果。本研究提出了一种用于情感分析的混合卷积神经网络-长短期记忆（CNN-LSTM）模型。我们提出的模型正在与 dropout、max pooling 和批量归一化一起应用以获得结果。对 Airlinequality 和 Twitter 航空公司情绪数据集进行的实验分析。我们采用了 Keras 词嵌入方法，该方法将文本转换为数值向量，其中相似词之间的向量距离很小。我们计算了各种参数，例如准确度、精度、召回率和 F1-measure，以衡量模型的性能。所提出模型的这些参数优于情感分析中的经典 ML 模型。我们的结果分析表明，所提出的模型在情感分析中的表现优于 91.3%。所提出模型的这些参数优于情感分析中的经典 ML 模型。我们的结果分析表明，所提出的模型在情感分析中的表现优于 91.3%。所提出模型的这些参数优于情感分析中的经典 ML 模型。我们的结果分析表明，所提出的模型在情感分析中的表现优于 91.3%。