# Sentiment Analysis of Twitter Data
## 3 Sentiment Analysis  
Opinion mining is a subfield of linguistics and natural language processing that deals with sentiment analysis. It evaluates the degree of polarity of words and phrases to examine and extracts views and feelings from textual data [20; 21]. Various studies and advances have been carried out by organizations or individuals that are interested in finding out how people feel about a given issue [20]. The term of sentiment was firstly coined by [PERSON] and [PERSON] [22] and [PERSON] [23] in 2001, who evaluated the sentiment of the market by automatic analysis of the text [9]. [PERSON] [24], [PERSON] et al. [25], and [PERSON] and [PERSON] [26] were some of the first to discuss sentiment analysis and the Natural Language Processing (NLP) methods that go along with it in their following publications. In addition, a great deal of work has been carried out on more application-oriented approaches. As an example, [PERSON] et al. [27] proposed a sentiment-based approach to forecast sale patterns. The models presented by [PERSON] et al. [28] to estimate product and merchant quality were statistical and heuristic. [PERSON] et al. [29] used sentiment analysis techniques to find hidden relationships between subjects and opinionated phrases in the political realm, where novel opinion scoring models were developed. [PERSON] and [PERSON] [30] sought to identify links between the number of comments and political sentiment using statistical modeling. Furthermore, evaluating Twitter conversation has emerged as a promising area of study. As the conversation offers a wealth of discriminative information relevant to various topics, it can facilitate the understanding of the feelings of people. Optimistic and pessimistic emotions expressed in Twitter conversations were analyzed by using a novel deep learning approach [31]. It integrated emotion detection with conversation reconstruction modules to discover sentiment polarity in social media posts. [PERSON] et al. [32] evaluated the cross-platform spreading of information by analyzing Twitter conversations. [PERSON] et al. [33] developed forecasting models to predict the prevalence of virus using the workload of Twitter conversations, which employed a latent variables-based searching technique.  
Sentiment analysis has also been applied to business and social studies. Companies like Google and Microsoft have recently built their own sentiment analysis systems to assist in their industrial and commercial activities [34]. TSA attempts to address the difficulty of evaluating the hidden meaning of tweets posted on Twitter, which is considered a new subject of sentiment analysis. There exist some challenges to TSA, the most significant of which is the restriction on message size. Due to the fact that a tweet contains no more than 140 characters, it is difficult to glean the sentiment contained within such a little amount of text. Meanwhile, the irregular textual representation on Twitter intensifies the complicatedness. Therefore, several concerns need to be addressed by the suggested TSA procedures [14]. Figure 1 shows the general operation flow of TSA.  
A sentiment analysis system often receives data from a variety of sources, such as blogs, comments, reviews, etc., in a variety of forms, such as XML, HTML, and PDF [35]. Techniques like tokenization, steaming, and stop-word removal are used to standardize and transform the data from the corpus into training datasets in text format. In sentiment analysis, selecting a collection of relevant features to train the text classifiers is a critical stage  
Figure 1: The operation flow of Twitter sentiment analysis [14].  
since different combinations of features have a significant impact on the final performance of sentiment analysis tasks. Then, the polarity label of the tested data is determined, relying on a text classifier which is trained and built up by the machine learning technique [14].