Analysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.

发现对社交媒体上发布的消费者评论的分析对于几种业务应用程序至关重要。社交媒体上发布的消费者评论数量和相关性都以指数级增长，这导致了大数据的产生。本文提出了两种深度学习架构的混合方法，即卷积神经网络（CNN）和长期短期记忆（LSTM）（具有记忆的RNN），用于在不同领域发布评论的情感分类。深度卷积网络在局部特征选择方面非常有效，而循环网络（LSTM）在长文本的顺序分析中通常会产生良好的结果。拟议的Co-LSTM模型主要针对情感分析中的两个目标。首先，它在检查大型社交数据时非常适用，并牢记可扩展性，其次，与传统的机器学习方法不同，它不受任何特定领域的限制。该实验已在来自不同领域的四个评论数据集上进行，以训练该模型，该模型可以处理通常在帖子中出现的各种依赖性。实验结果表明，提出的集成模型在准确性和其他参数方面优于其他机器学习方法。