# LDA-Based Topic Modeling Sentiment Analysis Using Topic/Document/Sentence (TDS) Model
## 1 Introduction  
Since 2000, a great deal of research has been conducted on consumers' opinions and sentiments due to the increase in online commercial applications. Business sectors and organizations have put substantial efforts in determining consumers' opinions about the product or service they offer. This is because customers' decision-making process is significantly affected by the opinion of people around them (friends, family, etc.). As [PERSON] et al. explained in [1], in research literature it is possible to see various names, e.g., \"sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, and review mining\"; however, all of them have a similar purpose and belong to the subject of sentiment analysis or opinion mining. Due to this, the importance of sentiment analysis (SA) is being realized in a range of different domains, such as consumer products, services, healthcare, political science, social sciences, and financial services [1, 2, 3]. A basic task in sentiment analysis is classifying the polarity of a given text at the document, sentence or feature level whether the expressed opinion in a document, a sentence or an entity feature is positive, negative, or neutral. Advanced, \"beyond polarity\" sentiment classification looks, for instance, at emotional states such as enjoyment, anger, disgust, sadness, fear, and surprise [4]. Meanwhile, with the rapid growth in the volumes of text data produced in recent years, the need for text mining has increased to discover hidden knowledge and insights from text data. Texts comprise meaningful words that describe people's opinions. Opinions are usually subjective expressions that describe people's feelings and sentiments toward entities,events, and properties [5]. Sentiment analysis, also known as opinion mining, is a task that uses natural language processing (NLP), text analysis, and computational techniques to automate the extraction or classification of general public's positive, negative, and neutral emotions about the products and services they have used. However, finding the sentiment polarity or opinion mining from a large amount of textual data is an overwhelming task. For example, there is a lack of capability in dealing with complex sentences; and the existing SA techniques do not perform well in certain domains and have inadequate polarity detection and accuracy. A detailed survey in [6; 7] explained the challenges of SA and possible techniques that can be used to solve each problem. ASA task is considered a sentiment classification problem [8]. Sentiment classification is one of the most commonly studied areas for most researchers. Sentiment classification techniques can be divided into machine learning (ML), lexicon-based, and hybrid approaches [9]. ML approaches use various linguistic features for sentiment classification. The lexicon-based approach implements a sentiment lexicon. The hybrid approach, as the name suggests, combines both approaches. Thus, sentiment lexicons play an important role in most sentiment classification methods. In terms of different levels, the classification can be divided into document-level, sentence-level, and aspect-based classification [10]. Sentence-level classification classifies sentiment expressions into every sentence. For this step, the subjectivity of the sentence is identified, and the classification process determines whether the sentence is positive or negative. An aspect-based level classifies the sentiments based on certain aspects of entities, such as identifying the entities and their aspects. To perform this analysis, supervised and unsupervised ML algorithms can be chosen. To address the aforementioned problems, we present a robust and reliable, real-time sentiment analysis framework based on unsupervised machine learning approach. A motivation for this work was developed by reading JST (Joint Sentiment Topic model to be explained subsequently) model implementation in sentiment classification.