# Exploiting Stacked Autoencoders for Improved Sentiment Analysis
## 2 Literature Review
### Sentiment Analysis  
The basic classification or clustering module of most classic sentiment analysis research papers relied on supervised machine learning approaches [16]. When displaying and classifying user-provided texts for emotional intent, these systems frequently used the BOW model and n-gram features [17]. N-grams are continuous sequences of words, symbols, or tokens in a document. The simple bag-of-words (BOW) model has a number of drawbacks, including the inability to account for word order and syntax [17]. In particular, the high-dimensional feature space generated by utilizing n-gram features is a major problem [18]. Many recent research studies [19; 20] have made substantial use of feature selection approaches to try to solve this issue. Linear discriminant analysis (LDA), support vector machine (SVM), naive Bayes (NB), and artificial neural networks (ANNs) are the most prevalent and most effective classification algorithms for detecting sentiment from user-generated text [21; 22; 23]. Some of the most common drawbacks of using these supervised algorithms are that they require a huge quantity of training data and are sometimes very slow. Unsupervised lexicon-based approaches have been proposed [17; 24] to overcome these issues. These approaches are straightforward, quick, and adaptable. However, because they rely so much on the lexicon approach, they are less accurate [24; 25] than those that are closely overseen. Furthermore, lexicon-based approaches are limited in their applicability to domains without a well-defined vocabulary because of their domain dependency. Few researchers have attempted to combine supervised and lexicon-based methods to benefit from them [26; 27]. For tweet sentiment analysis at entity level, [28] merged supervised and lexicon-based techniques. High precision and recall were obtained from lexicon and supervised methods, respectively. An approach for concept-based sentiment analysis that incorporates both lexical and machine learning methods has also been developed [29]. The created solution was superior to both purely lexicon-based methods and purely statistical methods for identifying polarity and sentiment strength, and it also provided a more convincing explanation and justification. A novel hybrid approach to sentiment classification was recently devised [30], with the promise of locating and paring down an acceptable Twitter-specific vocabulary collection. Last but not least, in [31], a hybrid machine learning/lexicon-based approach to sentiment polarity is presented. These authors compared the hybrid approach to statistical and lexicon-based classifications of user reviews, and found that the latter were superior, while the former were more accurate. Few studies have attempted to use hybrid DL models for sentiment analysis. In [32], a model for SA that is a fusion of long short-term memory (LSTM) and SVM is proposed. The IMDB movie review dataset was used for evaluation purposes. In [33], researcherscreated a hybrid heterogeneous support vector machine (H-SVM), specifically for SA, that combines features of both traditional SVMs and H-SVMs. Tweets related to COVID-19 were used to test proposed technique. In [34], researchers attempted to develop a generalized paradigm for sentiment analysis. In this research, the authors incorporated a convolutional neural network (CNN) into their algorithm for generating vectors from the review dataset. The dataset used for this study consisted of IMDB reviews (publicly available) and amazon reviews (collected by the authors). In [35], a hybrid model of CNN and SVM is proposed. fastText was used for embedding of Arabic text, CNN was used for feature selection and SVM was used as the final classifier. Researchers have suggested a hybrid model to deal with the problem of classifying the ambience of both users and content [36]. Social context features are embedded in the model. Various datasets were used for the evaluation of the model.