Hybrid Multichannel-Based Deep Models Using Deep Features for Feature-Oriented Sentiment Analysis

With the rapid growth of user-generated content on social media, several new research domains have emerged, and sentiment analysis (SA) is one of the active research areas due to its significance. In the field of feature-oriented sentiment analysis, both convolutional neural network (CNN) and gated recurrent unit (GRU) performed well. The former is widely used for local feature extraction, whereas the latter is suitable for extracting global contextual information or long-term dependencies. In existing studies, the focus has been to combine them as a single framework; however, these approaches fail to fairly distribute the features as inputs, such as word embedding, part-of-speech (PoS) tags, dependency relations, and contextual position information. To solve this issue, in this manuscript, we propose a technique that combines variant algorithms in a parallel manner and treats them equally to extract advantageous informative features, usually known as aspects, and then performs sentiment classification. Thus, the proposed methodology combines a multichannel convolutional neural network (MC-CNN) with a multichannel bidirectional gated recurrent unit (MC-Bi-GRU) and provides them with equal input parameters. In addition, sharing the information of hidden layers between parallelly combined algorithms becomes another cause of achieving the benefits of their combined abilities. These abilities make this approach distinctive and novel compared to the existing methodologies. An extensive empirical analysis carried out on several standard datasets confirms that the proposed technique outperforms the latest existing models.