## 2 Literature Review
### Studies on Sentiment Analysis  
Sentiment analysis has emerged as an important aspect of NLP. Sentiment analysis leverages a variety of NLP techniques to extract the sentiment expressed in texts and determine whether they are positive, negative or neutral [47; 48]. Analysis of text sentiments has spread across many fields such as consumer information, marketing, books, application, social media, tourism destination and hotels [49; 50; 51; 52; 53; 54; 55]. The approaches to sentiment analysis can be mainly divided into two types, namely, machine learning and lexicon-based methods. Machine learning methods represent documents as vectors in a feature space and classify them into predefined sentiment categories [56]. There are several machine learning methods for sentiment classification, such as naive Bayes (NB), maximum entropy and sup port vector machine (SVM). These classifiers usually use the bag-of-words (BoW) method comprising unigrams or n-grams to determine how the documents are represented [57], resulting in high dimensionality of the feature space. With the help of feature selection techniques, such as part-of-speech (POS) tagging which aims to disambiguate text sense based on a lexical category, machine learning algorithms can reduce high-dimensional feature space by eliminating the noisy and irrelevant features. Existing studies concluded that some machine learning classifier algorithms have better performance than lexicon-based [58; 59], but these methods have some certain defects: (1) classifiers trained for a domain-specific problem do not perform well in other domains [58]; (2) feature construction is critical but can hardly implement [60] and (3) these methods usually rely on a great volume of manually labeled training data [61]. Given these drawbacks, unsupervised methods like lexicon-based methods are applied. Lexicon-based methods are based on the assumption that the contextual sentiment orientation is the sum of the sentiment orientation of each word or phrase by matching a word or phrase with words from sentiment lexicon and their associated sentiment scores [62]. In general, adjectives are used as indicators of the semantic orientation of a text [58]. More recently, verbs and nouns are also used to compile into a sentiment dictionary [63]. Such a lexicon or dictionary can be created manually, or automatically, using seed words to expand the list of words [64]. [PERSON] et al. [65] built a lexicon for the Arabic language and the proposed approach gained better accuracy than other methods. [PERSON] et al. [58] constructed a dictionary incorporating intensification and negation to compute text sentiment scores, which is called the semantic orientation calculator (SO-CAL) approach. [PERSON] et al. [66] developed an n-gram sentiment dictionary called Senti-N-Gram for automatic score calculation. Compared to machine learning methods, sentiment lexicons learned from a certain domain preserve the domain-based orientation of words, which provides greater accuracy for sentiment analysis tasks [67]. Furthermore, lexicon-based methods take the lexical and syntactical information in linguistic content into account in order to revise the sentiment valence [56]. That is, in the sentiment scoring process, negation, intensification, and the rhetorical roles of text segments are taken into account as well. The language-dependent features can also be considered in lexicon-based model [68]. In summary, machine learning sentiment analysis trained on a particular dataset by using features, which may reach quite high accuracy in detecting the polarity of a text. However, this is highly dependent on labeled data, limiting its application. Unsupervised lexicon-based methods, such as knowledge-graph propagation and seed word-based methods, not only overcome the absence of labeled data, but are able to extract domain-specific sentiment words [69; 70].