## 2 Related Work
### Lexicon-Based Techniques, N-Grams and Natural Language Processing  
The lexicon-based techniques use a dictionary of words labeled with their sentiment orientations. In such techniques, a piece of text is converted into a bag of words whose sentiment orientations are summarized or aggregated to classify the text. This technique is simple, but it is mostly dependent on manual labeling of the text [22]. [PERSON] and [PERSON] [23] suggested that sentence structure and contextual information are important for sentiment orientation and classification. In their work, each term in the sentence was assigned a sentiment score from the Sent WordNet lexicon. The overall classification of the sentence is the sum total score of the individual scores of the terms in the sentence. While the approach is interesting, one of the limitations of this approach is that words can be of the same orientation, but negating one another, thus giving the wrong sentiment classification. The main improvements of lexicon-based techniques involve using lexicon labeled words as input to machine learning classifiers. [PERSON] et al. [24] combined lexicon-based approach and support vector machine. In their method, they generated word sentiment labels and used them as input to the SVM classifier. [PERSON] et al. [14] used several lexicons to assign lexicon vectors to words in a text, which they referred to as Lexicon2 Vec (L2V). They combined their vector with Word2 Vec and PoS2 vec to obtain a hybrid vector representation. Generally, little research has been performed on combining lexicon-based methods and deep learning architectures. [PERSON] et al. [25] proposed a sentiment analysis model of online reviews, which they referred to as polymerization topic sentiment model (PTSM). In their model, they used lexicon dictionary to extract sentiment information from online reviews. Although their model performed well with the support vector machine, they did not test their model with deep learning classifiers or word embedding algorithms. However, they recommended use of lexicon-based methods to solve the over-fitting problem of sentiment analysis models and to filter unnecessary information  
Generation of word N-grams is another important NLP technique applicable in sentiment analysis. In text classification, word-grams are used to generate word co-occurrence patterns and vectors for machine learning classifiers. N-gram NLP models are widely used due to their simplicity and effectiveness [26]. However, they do not consider the information encapsulated in the sequence of the words. For instance, words could be negating one another in a sentence or having different meaning in different context. [PERSON] et al. [27], in their recent research on use of N-grams in text representation, used bi-grams and tri-grams to extract features from text data. Their work yielded promising results, which is an indication that N-grams can be utilized for effective text representation. They proposed a big data analytics framework for sentiment analysis and classification using intelligent cognitive inspired computing. In their model, they used fuzzy cognitive maps as classifiers. In our research, we advance this work by investigating use of hybrid NLP techniques, including N-grams and sentiment lexicon. They also recommended future research on deep learning architecture, an area which is also being explored in this research work. We do so by seeking to combine pre-trained word embeddings with sentiment lexicon and N-grams.