Rapid development in information technology (IT) and the internet has made people not only retrieve the information but express their opinions as well. There is a different platform available for people to express their opinion; opinions like online reviews comprises valuable information for consumers as well as business. A large amount of data is accumulated through these opinions over the internet, opinions, and sentiment can be extracted through NLP and big data analysis [1]. Sentiment analysis is a process of understanding text meaning and analyzing the sentiment hidden in a given text, NLP is one of the popular technology to model disassemble reason extract, and classify. The main aim of sentiment analysis is to identify the attitudes of bipolar views based on targets in sentences [2].

Sentiment analysis is categorized on three distinctive levels that are shown in Fig. 1 and discussed further. Figure 1 A.

Document-SA: In this type of task, the aim is to determine the positive or negative opinions based on overall documents. It is one of the simplest ways to analyze sentiments. This task considers each document expresses an opinion on a single entity. However, it does not hold for a scenario where multiple views are evaluated in a single document.

Aspect-based-SA is referred to as the task of identifying the various aspect in review and distinguishing the sentiment polarities (SP) towards aspects in an individual manner for extracting the fine-grained information. ABSA task possesses wide research value as it enables consumers to an evaluation of product or service from a comprehensive perspective to understand detailed and explicit knowledge of it [2]. Moreover, Neural network-based mechanisms have made a significant impact in comparison with the traditional approach of sentiment analysis. Recent studies on neural networks (NNs) have observed significant growth in Natural Language processing such as text summarization and machine translation, however still ABSA–NN required to be studied in depth [3]. ABSA is categorized into two tasks, mining (aspect mining) and classification (aspect classification); aspect mining tends to extract the aspect words from each sentence, aspect mining has been exploited through unsupervised models, semi-supervised and supervised model whereas aspect classification aims at polarities prediction on the aspect. However, getting accurate sentiment is a major challenge in sentiment classification; for instance, “Food was delicious, but the place was small”. In this sentence, there are two distinctive aspects i.e., food and place and opinion words like “delicious” and “small “corresponds to polarity where “delicious” is positive sentiment polarity towards food and “small” is negative sentiment polarity towards the room.

Fig. 2 shows the general framework followed by the research for classification of aspect-based sentiments, this general framework comprises three-phase namely aspect extraction, aspect sentiment analysis, and sentiment evaluation. Furthermore, apart from these three important blocks it also includes pre-processing steps such as discarding unnecessary information from corpus, tokenization stemming, and so on [4].

Through research it was observed that conventional sentiment models fail to observe the dynamic polarity recognition due to bilateral information lack, also emotions accompany different emotions. Moreover, in classification, 40% of error occurs when aspects are not considered. In recent years, deep neural networks (DNN) have achieved great success in several applications. Deep learning is classified into convolutional neural networks (CNNs) [5], recurrent neural networks (RNNs) [6], reconvolutional neural networks (ReCNNs) [7], and memory networks (MNs) [8], detailed classification of deep learning is shown in Fig. 3

Moreover, Fig. 3 includes the direct deep learning architecture adopted for Aspect-SA, in addition to that attention mechanism has been adopted widely along with the above-mentioned architecture to enhance the feature extraction and context [9]. Other types of Aspect-SA mechanisms include the embedding approach and the transfer learning approach. Furthermore, considering the deep learning architecture, CNN is considered effective and possesses the capability to extract meaningful patterns. However, CNN fails to balance the contextual information and sequential order, this problem can be solved through RNN. Hence recent study on Aspect-SA has focused on RNN, all basic RNN model considers the particular situation, which is formed through many words, however, the target is not restricted to a single word. Thus, attention with long short- term memory (LSTM) is used where targets and context are into LSTM to understand the context, however, it is restricted to single instances [9]. Bidirectional-LSTM is introduced which comprises embedding layer attention layer, contextual layer, and output layer, aspect, and context can be embedded into to get the aspect and contextual output. Hence Bi-LSTM architecture has been exploited widely to exploit the Sentiment analysis, meanwhile, Bi-LSTM based architecture also has a limitation, and considering the review of other research work (discussed in related work), several challenges still exist such as ignoring the other data types such as emoji as it is heavily used by user; also classifying sentiments when there are different targets, pre-processing of data could also improvise accuracy [4]. Other major challenges are deep knowledge of semantic information and context knowledge; thus, this research work focuses on deep semantic information and deep context knowledge [2,10].

Word expresses various sentiments as it holds various semantics in various contexts; for instance, the word “blue” holds different semantics in sentences like “He is so blue that none going to get better” or “I bought blue jeans” or She said it was just a blue. Each sentence conveys different sentiments; moreover, the existing embedding mechanism tends to directly embed the lexicon to word representation this makes no difference in words in different sentences, this fails to provide proposed information of words in various sentences to utilize absolute embedding. Hence motivated by its application and challenges, this research work proposes DSCNet architecture. A further contribution of research work is given below.

DSCNet architecture incorporates semantic information and contextual knowledge in deep to enhance the aspect extraction and understanding of context.

This research work is organized in the following way: the first section starts with an understanding of sentiment analysis, its types, and different challenges and approach used. This section concludes with the motivation and contribution of research work. The second section discusses different related work based on aspect-based sentiment analysis, third section designs and develops a neural network-based mechanism. The fourth section evaluates the proposed methodology considering different online reviews dataset.

Aspect Based Sentiment analysis is a basic task in SA field; it comprises two subtasks i.e., extraction and classification. ABSA considers targets and sentiments both at one time and the aim is for polarity determination of given aspect and sentence. Furthermore, with the development of NLP, SA has been given wide attention by researchers, this section discusses different aspect-based-SA work along with their shortcomings.

In general Aspect based-SA is categorized into the category of lexicon based [11], machine learning (ML) [12] based, and DL-based. A traditional approach such as parts of speech (PoS) [13] and bag of words (BoW) (Bag of Words) [14] focus on utilizing the feature engineering phenomena; also for training machine learning classifiers were used such as NN, support vector machine (SVM) and Naïve Bayesian. Above discussed approach is dependent on handcrafted features and thus it is labor-intensive. Further, the deep learning approach has been an evolutionary phenomenon for sentiment analysis through word embedding, here we discuss some of the recent successful research along with their shortcoming. In [15], Bidirectional gated recurrent unit (GRU) model was developed for complete feature mining for the ABSA task, Bidirectional GRU network is used for acquiring the dependency-based sentence SA along with their corresponding terms. Further, it proposes the learning feature of sentiment polarity in given sentences; despite enhancement in feature mining, accuracy did not improve much, and also it was proven to be time-consuming. Hence [16] developed a novel integration method of CNN and GRU to utilize the local features for efficient processing. Moreover, GRU is used for long-term dependency learning and CNN is used for the generation of local features. However, this model faces the issue of dependency learning which is enhanced in [17], it introduced dependency-related phenomena to identify the dependency-related feature for given aspect term; dependency parse tree is designed and dependency-related feature is integrated into BiLSTM and CNN. Furthermore, this research work observed that sentiment features of a given aspect help in discriminating the sentiment polarity. Designing a tree makes the model too complicated for enhancement in dependency-related phenomena. In [18], a model named effective adaptive transfer network (EATN) was presented, EATN utilized the understanding of the incorporation of correlation between different domains. Furthermore, it proposes domain attention module (DAM) for learning the common feature from a given source domain and further targeting the classification. DAM is designed for two tasks first is the sentiment classification task for sentiment knowledge and the second is domain classification. To reduce the feature dependency multiple kernel mechanism is selected. This mechanism faces an issue with labeling, thus [19] introduces Ecolabels which is a semiautomatic mechanism for the textual ER to provide the large-scale annotation considering English emotion corporate in different generations aiming robust reliability. It comprises two steps, first step includes the automatic process of pre-annotation of the unlabeled sentence along with optimized emotional categories. The second step includes a manual process of refinement where humans determine which is more dominant emotions in given pre-defined possibilities. Further, [20] proposed CNN along with a sentiment module known as SentiCNN, SentiCNN analyses different sentiments in sentences with sentiment and contextual information of words. Contextual words are identified through word embeddings and sentiment information is identified through lexicons. Moreover, a highway network is incorporated for combining the contextual and sentiment information from given sentences through developing the connection among features of sentences and words; also attention mechanism is used based on lexicons to identify the eminent sentiment indicators to make more effective predictions. [21] adopted different mechanism of end-to-end segmentation model named SEGBOT, SEGBOT uses BiRNN for encoding input text sequence, alter another RNN is used along with pointer network to identify the text boundaries in the given input sequence, hence it is not dependent on handcrafted feature and SEGBOT handles variable size issue and sparse boundary tags. To evaluate the model, document level and sentence level analysis are considered.

There have been enormous efforts in the fields of ABSA, however, still there exists research gap analysis that needs to be solved. These gaps include improvisation in the extraction capability of the ABSA model. Hence, in this research work, we used deep semantic and deep contextual knowledge for extraction and contextual understanding. The architecture formulation of DSCNet is designed in the next section of the research work.

The deep learning approach has shown wide application in ABSA task classification as deep learning exploits the aspect and context to classify the sentiment polarity. Hence, this research adopts the deep learning domain and introduces DSCNet. DSCNet exploits the aspect and target sentiment.

Fig. 4 shows DSCNet architecture, it comprises deep features, deep attention layer, DSCNet and Deep Integration layer. DSCNet is primary component of our research model, and it is capable of delivering the contextual knowledge of an aspect that has similar meanings through deep exploration of its features.

Sentiment analysis, also known as opinion mining is one of the fundamental tasks in natural language processing; it offers to infer opinions from given text. This research work develops a proposed methodology for Aspect Based Sentiment Analysis. In this section of the research, we evaluate the proposed methodology considering different parameters; evaluation is carried out considering deep learning library PyTorch. The proposed model is designed using python as a programming language and spyder as IDE on the Windows 10 platform packed with 8GB of RAM and 2GB of NVIDIA graphics. DSCNet is evaluated considering precision, recall, accuracy and macro-f1 (these metrics have been discussed later in same section), also comparative analysis is carried out on accuracy and Macro-F1 with other model.

Aspect-SA (aspect based sentiment analysis) is an indispensable task in sentiment analysis and subtask in natural language processing. with the development of NLP, research on sentiment analysis has been processed mainly on deep learning from past few years as deep learning architecture provides deep feature extraction, however considering the vulnerabilities of sentiment polarity, existing deep learning architecture fails to understand the context of an aspect which could be solved through deep context knowledge. This research work introduces DSCNet (deep semantic and deep contextual knowledge) architecture, DSCNet architecture incorporates Semantic information and contextual knowledge together in deep to enhance the aspect extraction and understanding. DSCNet have three modules i.e., deep extraction, updation and deep integration which aims for optimal aspect based feautures. DSCNet is evaluated on three customer reviews dataset, two reviews dataset of laptop and restaurant from semeval 2014 task B and ascl Twitter dataset. DSCNet model efficiency is proven through computing deep learning metrics like precision, recall, accuracy and Macro-F1. Furthermore, comparative analysis is carried out with syntax based and syntax free technique along with recent deep learning model and our model is proven to achieve better metrics than these model. Although DSCNet achieves marginal improvisation over the other deep learning architecture, several research areas need to be looked into such as considering dynamic dataset which includes sarcastic comments.