## 2 Related Work
\begin{table}
\begin{tabular}{c c} \hline \hline High Ranking Pseudo-Labeled Based Sentiment Learner & HRPL-SL \\ Active-Self-Training Based Sentiment Learner & AST-SL \\ Active-Self Co-Training-Based Sentiment Learner & AST-SL \\ Classification Fusion & CF \\ Lexicon Scoring & LS \\ Sampling by Clustering & SBC \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._In self-training, the most confident unlabeled examples, together with their predicted label with a given threshold are selected to add to the training set. [PERSON] et al. [34] addressed self-training, for sentence subjectivity classification. They adapted Value Difference Metric (VDM) as the selection metric in self-training. According to their experimental results, self-training with NBTree and VDM outperformed self-training with other combinations of classifiers selection metrics. [PERSON] et al. [28] presented the SELC Model for sentiment classification, which successfully integrates a corpus-based model with a lexicon-based approach. Active learning is an encouraging process for SSL classification, which reduces the data labeling cost. [PERSON] et al. [41] proposed a novel Active Deep Network (ADN) method for textual sentiment classification. [PERSON] et al. [51] proposed active learning for imbalanced sentiment classification. They utilized two complementary classifiers where the first classifier was used to obtain the most certain samples and the second to obtain the most uncertain samples for manual annotation. [PERSON] et al. [33] proposed a novel learning model in which they combine active learning with self-learning to decrease the effort of human labeling and improve the classification performance in cross-lingual sentiment classification. In their model, firstly, unlabeled data translated from the target language into the source language, then translated data are increased into initial training data in the source language utilizing active learning and self-training. They also considered the density measure of unlabeled examples in active learning to avoid the selection of outlier examples from unlabeled data. Co-training [27] is a bootstrapping method that assumes that feature space can be divided into two different redundant and sufficient sets (views). Initially, two different classifiers are trained with the labeled data on the two different views, respectively. Each classifier then applied to the unlabeled data, and the most confident predictions of each trained classifier on the unlabeled data are added into the training set of another classifier for label data expansion. [PERSON] et al. [31] proposed a dual view co-training algorithm based on dual view bag-of-words (BOW) representation for semi-supervised sentiment classification. In dual view BOW, they automatically constructed antonymous reviews by a pair of bag-of-words with opposite views. They made use of the original and antonymous views pair-wisely in training, bootstrapping and testing processes, all based on a joint observation of two views. [PERSON] et al. [37] presented an LCCT (Lexicon-based and Corpus-based, Co-Training) model for semi-supervised sentiment classification, combining the idea of lexicon-based learning and corpus-based learning in a unified co-training framework. [PERSON] et al. [29] proposed a cooperative semi-supervised learning approach based on the hybrid mechanism of active learning and self-learning for textual sentiment classification. Recently, deep learning-based aspect extraction [52], attention-based LSTM [53] and capsule networks [54; 55] have also been widely used for sentiment analysis and challenging NLP applications that yield state-of-the-art prediction results. In this work, our focus is on the design of a unified sentiment analysis framework for multi-domain textual sentiment classification. In literature, for SA, SSL has been used by many researchers while gaining performance [29; 30; 31; 41; 51].