## 1 Introduction  
Sentiment classification or opinion mining is the narrow field of natural language processing, information querying, and text mining that is used to extract a person's impressions of or thoughts about something from nonstructural text data. This research domain has drawn the interest of not only scientists but also businesses and organizations worldwide. The ability to classify sentiment has a practically tremendous impact practically because it helps businesses save the expenditure of human resources to determine customers' needs while helping customers choose more suitable products and services according to their necessities. [PERSON] and [PERSON]'s [1] survey reported that more than 80% of internauts search at least once for reviews about a product they intend to buy before making their decision. The problem of sentiment classification was raised by [PERSON] et al. [2] and [PERSON] and [PERSON] [3] in the early 2000s. Since then, many research studies have been conducted to classify and evaluate reviews about products and services in media and blog posts, which can be classified into three levels of interest: (i) document level; (ii) sentence level; and (iii) aspect level. On the first level, [PERSON] et al. 's model [4], based on a deep learning approach, and [PERSON] et al. 's ensemble learning model [5] should be mentioned. With reference to the sentence level, [PERSON] et al. [6] and [PERSON] and [PERSON] [7] proposed models based on conditional random fields (CRFs). However, the aspect level has received the most consideration within various publications. The focus of this paper is on [PERSON] and [PERSON]'s [8] and [PERSON] et al. 's [9] work, which proposed a syntactic-based approach using dependency grammar. Techniques used for classifying sentiments can be put into three main groups: machine learning comprising [PERSON] et al. [10], [PERSON] et al. [11], and [PERSON] et al. [12]; relying on vocabulary with [PERSON] et al. [13], [PERSON] et al. [14], and [PERSON] et al. [15]; and a hybrid of machine learning and vocabulary with [PERSON] et al. [16] and [PERSON] and [PERSON] [17]. Machine learning techniques consist of supervised machine learning like [PERSON] et al. [18], semi-supervised machine learning like [PERSON] et al. [19], and unsupervised machine learning like [PERSON] and [PERSON] [20]. Techniques that rely on vocabulary comprise three groups referring to some noticeable works as follows: a vocabulary approach with [PERSON] et al. [21], a dataset approach with [PERSON] et al. [22], and integration of the above ones with [PERSON] et al. [23]. In the last two decades, machine learning methods have dominated majority of sentiment analysis tasks. Since feature representation greatly influences the performance of a machine learning algorithm [24], many researches focus on getting effective features in-hand with domain expertise and ad hoc techniques. However, this work can be completed using representation learning algorithms, such as the deep learning approach, which automatically distinguishes and explains text representations from data. Deep learning has emerged due to its ability to represent data at various classified levels. [PERSON] et al. [25] and [PERSON] et al. [26] are two remarkable evidences of this approach. In regard to valence shifters in sentiment analysis, users often produce reviews about subjects based on the sentiment levels. Thus, the sentiment value of a phrase can be affected by the corresponding context, which is called polarity shifting (or valence shifters) [27]. Polarity shifting bears complex language structures which consist of negative, contrasting, intensified, and diminished structures [23]. Polarity shifting can make traditional approaches, such as machine learning with the Bag-of-Words (BoW) model, ineffective because these approaches are interested in whether single words bear positive or negative polarity, based on a predetermined sentiment dataset.