# LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
## 2 Related Works
### Artificial Intelligence Approaches for Sentiment Analysis  
In recent years, DL approaches have been significantly and effectively implemented in sentiment analysis research areas in different ways. In contrast to the techniques reviewed earlier that rely on handcrafted characteristics, ML and DL approaches can automatically identify, extract, quantify, and analyze complicated point features. Another benefit is that deep neural networks can be implemented flexibly and successfully in automatic feature extraction using learned data or analyzed customer's feedback data; instead of spending time extracting functions, they can be modified to create a robust database and an appropriate network structure.  
There has been a great deal of research focus on the problem of sentiment classification. Supervised and unsupervised ML approaches are separately used by most researchers to classify SA. In addition, supervised and unsupervised approaches can also be combined to analyze the sentiment. In [18], authors used supervised and unsupervised methods together in the case of proposing meta-classifiers to develop a polarity classification system. Furthermore, in [19], unsupervised learning algorithm was applied to automatically categorize text, which resulted in training sets by using keyword lists. They classified documents into a certain number of predefined categories. The purpose of their study was to overcome the problems associated with the creation of labeled training documents and manually categorizing them. To evaluate the proposed methods, they embodied a traditional system by supervised learning using the same naive Bayes classifier, and then tested and compared the performance check.  
Similar research was conducted by [PERSON] et al. [20], applies an unsupervised learning algorithm to classify the semantic orientation regarding mutual information between document phrases and a small set of positive/negative paradigm words. [PERSON] et al. [21] proposed a framework based on the LDA approach called JST to detect sentiments and topics simultaneously. Their JST model is also an unsupervised ML algorithm. In another interesting study, [PERSON] et al. used a statistical approach in the feature selection process as detailed in [22]. The hidden Markov model (HMM) and LDA method were used to separate the entities in a review document from the subjective expressions according to the polarity of those entities. The proposed scheme achieved competitive results for document polarity classification. Although some approaches have applied unsupervised and semi-supervised learning methods [23; 24], using supervised learning techniques for aspect-based sentiment analysis is also a very popular concept in machine learning [25]. The LDA model allows documents to be explained by latent topics. There is also a very applicable supervised ML approach called latent semantic indexing (LSI) [26]. LSI is a well-known feature selection method that attempts to reduce the dimensionality of the data by transforming the text space into a new axis system. However, compared with LSI, LDA has a better statistical foundation to define the topic-document distribution \(\theta\), by allowing inferences in new documents based on previously estimated models, and avoids the overfitting problem.