## 2 Related Work
### Machine Learning-Based Approach  
The machine learning-based approach employs a supervised learning technique to train a text classifier using a manually labeled training dataset and extract the features. More specifically, the process can be described as follows. Given a training dataset consisting of labeled text documents, \(D=\{(d_{l},l_{1}),\ldots,(d_{m},l_{m})\}\), where \(d_{i}\) represents a document belonging to the initial training corpus, \(D\), and \(l_{i}\) is the manual label reflecting a specific preset class among the set of classes, \(L\). Here, the main task is to detect unlabeled text data by generating an effective classification model from the labeled training dataset [19]. Figure 1 depicts the structure of Twitter sentiment analysis based on the machine learning approach. Various formats of sources such as blogs, reviews, etc., are utilized as input data for Twitter sentiment analysis. Here, useful features are selected by a feature extractor for subsequent processing, which is critical to sentiment analysis. This is because the combination of different features plays a crucial role in the classification. The classifier is then trained by a machine learning algorithm to evaluate the degree of polarity and assign a corresponding label to the tested instance [25; 26; 27]. Various learning algorithms have been adopted for the machine learning-based approach, including Support Vector Machine (SVM), Decision Tree (DT), Naive-Bayes (NB), and NN, etc. SVM has been proved to be a powerful classification algorithm, especially in processing a massive number of features [28; 29; 30]. However, the weakness of low interpretability and computational overhead limits its usage. DT is another popular algorithm where the features and corresponding classes are reflected in the form of tree structure. It is well understood, and can be easily employed to classify textual data. However, exploring the features is restricted owing to its rigid tree structure, and feature overfitting may degrade the performance [31; 32; 33]. NB is a commonly used method for sentiment analysis due to its advantages of effectiveness and simplicity. It is used to select the category of the highest probability for the unlabeled text based on the probability theory [19; 34; 35; 36]. The NN classifier employs a network structure that is composed of a group of connected units, where each input represents a term and an output is generated from a function to represent the corresponding category. The dependence relations are represented by weighted edges between the units. Assume that a tested document_d is evaluated by the NN classifier. The term weight, \(w\), is viewed as an input to the units, and the output of the units is the decision of the category that is obtained by activating the units of the network [37]. Perceptron is a simple linear NN classifier, which is also called a single-layer perceptron, which applies the Heaviside step function to activate the units of the network [38]. Another type of linear NN classifier incorporating logistic regression achieves good classification performance [39]. Here a non-linear NN classifier is implemented by constructing one or more layers of units. In [40] a non-linear NN was applied to the task of topic spotting, which models higher-order interaction between the terms. Due to the advantage of the NN classifier in processing data with multiple attributes, recently, many researches have been focusing on the study of the NN technique. A novel NN model (ConvGRNN and LSTM-GRNN) was proposed to evaluate the document-level sentiment analysis, where the semantics of sentences and corresponding correlations are encoded by the document representation [41]. In [42] a Mixed Neural Network (MNN) was introduced to solve the classification problem, which combines a rectifier Neural Network and Long Short-Term Memory (LSTM). A neural architecture and two extensions of Long Short-Term Memory (LSTM) [43] were presented to handle the task of targeted aspect-based sentiment analysis.