Sentiment analysis focuses on identifying and classifying the sentiments expressed in text messages and reviews. Social networks like Twitter, Facebook, and Instagram generate heaps of data filled with sentiments, and the analysis of such data is very fruitful when trying to improve the quality of both products and services alike. Classic machine learning techniques have a limited capability to efficiently analyze such large amounts of data and produce precise results; they are thus supported by deep learning models to achieve higher accuracy. This study proposes a combination of convolutional neural network and long short‐term memory (CNN‐LSTM) deep network for performing sentiment analysis on Twitter datasets. The performance of the proposed model is analyzed with machine learning classifiers, including the support vector classifier, random forest (RF), stochastic gradient descent (SGD), logistic regression, a voting classifier (VC) of RF and SGD, and state‐of‐the‐art classifier models. Furthermore, two feature extraction methods (term frequency‐inverse document frequency and word2vec) are also investigated to determine their impact on prediction accuracy. Three datasets (US airline sentiments, women's e‐commerce clothing reviews, and hate speech) are utilized to evaluate the performance of the proposed model. Experiment results demonstrate that the CNN‐LSTM achieves higher accuracy than those of other classifiers.

中文翻译：

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使用统一的卷积神经网络-长短期记忆网络模型进行推文情感分析

情感分析专注于识别和分类文本消息和评论中表达的情感。诸如Twitter，Facebook和Instagram之类的社交网络会生成大量充满情感的数据，当试图同时提高产品和服务的质量时，对此类数据的分析非常有成果。传统的机器学习技术在有效分析大量数据和产生精确结果方面的能力有限。因此，它们受到深度学习模型的支持，以实现更高的准确性。这项研究提出了卷积神经网络和长短期记忆（CNN-LSTM）深度网络的组合，用于对Twitter数据集进行情感分析。使用机器学习分类器（包括支持向量分类器，随机森林（RF），随机梯度下降（SGD），逻辑回归，RF和SGD的投票分类器（VC）以及最新的分类器模型。此外，还研究了两种特征提取方法（术语频率-逆文档频率和word2vec），以确定它们对预测准确性的影响。利用三个数据集（美国航空情绪，女性电子商务服装评论和仇恨言论）来评估所提出模型的性能。实验结果表明，CNN-LSTM比其他分类器具有更高的准确性。还研究了两种特征提取方法（术语频率-逆文档频率和word2vec），以确定它们对预测准确性的影响。利用三个数据集（美国航空情绪，女性电子商务服装评论和仇恨言论）来评估所提出模型的性能。实验结果表明，CNN-LSTM比其他分类器具有更高的准确性。还研究了两种特征提取方法（术语频率-逆文档频率和word2vec），以确定它们对预测准确性的影响。利用三个数据集（美国航空情绪，女性电子商务服装评论和仇恨言论）来评估所提出模型的性能。实验结果表明，CNN-LSTM比其他分类器具有更高的准确性。