Sentiment analysis or opinion mining is used to understand the community’s opinions on a particular product. This is a system of selection and classification of opinions on sentences or documents. At a more detailed level, aspect-based sentiment analysis makes an effort to extract and categorize sentiments on aspects of entities in opinion text. In this paper, we propose a novel supervised learning approach using deep learning techniques for a multitasking aspect-based opinion mining system that supports four main subtasks: extract opinion target, classify aspect, classify entity (category) and estimate opinion polarity (positive, neutral, negative) on each extracted aspect of the entity. We have used a part-of-speech (POS) layer to define the words’ morphological features integrated with GloVe word embedding in the previous layer and fed to the convolutional neural network_bidirectional long-short term memory (CNN_BiLSTM) stacked construction to improve the model’s accuracy in the opinion classification process and related tasks. Our multitasking aspect-based sentiment analysis experiments on the dataset of SemEval 2016 showed that our proposed models have obtained and categorized core tasks mentioned above simultaneously and attained considerably better accurateness than the advanced researches.