## 2 Related Work  
Recently several SML, SSL and lexicon-based approaches for textual SA have been proposed [7; 12; 34; 42; 43]. The SML approach relies on the presence of labeled training data to learn a classifier like SVM, which is subsequently applied to the test data for classification. An SML approach for movie review sentiment classification was proposed by [PERSON] et al. [7]. They applied three SML algorithms (NB, Maximum Entropy and SVM) and achieved a high accuracy of 82.9% using unigrams as features. Another SML approach was presented by [PERSON] et al. [44], in which they applied SVM for testing diverse domains of datasets and used various weighting schemes (TFIDE, BO, TO) while adopting n-gram based approach. They obtained a high accuracy level of 91.5% using unigram features with TFIDF scheme. SML approach for sentiment classification performs well when enough amount of label data is available. However, it is difficult to get a large amount of labeled data manually for different domain. This deficiency brought an interest to lexicon based and SSL approaches for SA. Various sentiment lexicons with prior polarity have been created recently for document classification. Sentiment lexicon assists as a vital tool for making a sentiment classification system [19]. Sentiment lexicon is a list of opinioned words or phrases. In some cases, a value is assigned to each word in the sentiment lexicon to differentiate the semantic orientation level of the word. Some of the most well-known and largely used general sentiment lexicons are AFFIN, OL, MPQA, SO-CAL, Subjectivity lexicon, NRC Hashtag Sentiment Lexicon, etc. According to the existing literature, the general sentiment lexicons have been generated manually, semi-automatically or fully automatically [45]. The manual approach of sentiment lexicon generation is very difficult due to high effort and expert time requirements. The manual approach for sentiment lexicon development is usually combined with automatic approaches. [PERSON] et al. [18] have developed a manually compiled sentiment lexicon for sentiment analysis. Generally, there are two popular methods for sentiment lexicon construction, dictionary-based method, and corpus-based method [4]. The dictionary-based method utilizes an initial set of seed sentiment words, which are collected and labeled manually. This set is then expanding by looking synonyms and antonyms in thesauri such as WordNet [46]. The corpus-based approach utilizes seed sentiment words to discover new domain sentiment words and their orientation [4]. [PERSON] et al. [47] exploited some seed sentiment words for discovering new domain sentiment words in the corpus. Another corpus-based method relies on the word co-occurrence in the corpus [48]. Most of the existing general sentiment lexicons for sentiment classification is insufficient due to the limited word coverage that may neglect important sentiment words, which are not present in the existing lexicons. SSL approach can take benefit of utilizing a small amount of labeled training data, together with a large amount of unlabeled data. There is a reason for using unlabeled data to boost the learning performance of a learner in a case when only a small-scale of labeled data exists [27]. In the SSL approach, an initial set is used for training the first classifier. The selection of an initial set for training first classifier is significant. In previous studies, the selection of an initial set from the whole unlabeled data is made by random sampling. However, due to the complex distribution of data, random sampling cannot ensure selection of the most representative instances. Therefore, other techniques like sampling by clustering and sentiment lexicon are used for initial training set generation [14; 49; 50]. In the literature, many SSL techniques such as self-training, co-training, active learning and graph-based and topic-based semi-supervised learning have been used for classification problems [27].