# Sentiment Analysis of Twitter Data
## 1 Introduction  
Due to the recent explosive rise of Social Networking Services (SNS), an enormous amount of user-generated data, such as comments and reviews, is being created consistently [1]. People's opinions and feelings are expressed in the information, which is mostly based on a common object of interest. These data have become treasure troves of information, giving several chances for analyzing people's reactions, which is particularly beneficial in forecasting the sales of products [2], trends in the stock market [3], and results of political elections [4]. There are more than 300 million active Twitter users [5], making it one of the most popular micro-blogging services [6]. In light of its significance in the perception of people's thoughts and attitudes, Twitter-based Sentiment Analysis (TSA) has consequently attracted a great deal of attention [7, 8]. The topic of SA has been the subject of a great deal of writing, and more recently, significant attention has been paid to TSA. Obviously, this therefore calls for a survey article that may provide an overview of the current techniques and directions in the field of study. [PERSON] and [PERSON] [9] provided an extensive and in-depth review of SA through experimental works by using different kinds of data. However, the most up-to-date methods were not shown in the article due to the fact that it was released a while ago. In addition, comprehensive coverage of core concepts and topics concerning SA was introduced by [PERSON] et al. [10], in which the examination of application-centric methods was performed to explain the basic ideas of SA. [PERSON] et al. [11] offered a survey providing a brief introduction to the techniques of TSA. Nevertheless, only a few publications were mentioned. Although there is also a most recent survey related to TSA [12], in which only the machine-learning-based methods were investigated. According to our knowledge,there is a lack of comprehensive studies focusing on TSA. Thus, as a fundamental, a thorough overview of the concepts of SA, and a more concise description of the ideas and termininologies of TSA was illustrated in this survey. Recent advances and discoveries in TSA were also presented. Moreover, tables were used to properly classify the published papers, which allows for a more straightforward comparison among various methods. The chosen articles in the present survey have a significant impact on TSA research and related topics. Particularly, the state-of-the-art technologies available today have been incorporated to exhibit the most current findings of TSA, while the traditional approaches were selected as a comparative standard. In addition, the central section of the survey is structured with three primary components: machine-learning-based, lexicon-based, and hybrid approaches, all of which are in keeping with the current trends in TSA research. More effort has also been devoted to machine-learning-based solutions since those techniques can produce a better performance of prediction accuracy for TSA tasks. Specifically, TSA is extensively discussed in this survey, and it is broken down into the following subsections: Section 2 introduces the role and the structure of Twitter. Section 3 illustrates the background and basic concept of sentiment analysis. The representation of the feature for TSA is explained in Section 4, and Section 5 shows the different levels of analysis. In Section 6, the approaches and recent achievements in Twitter sentiment analysis are presented. Section 7 presents several survey-related discussions. Finally, the survey is concluded in Section 8. Table 1 displays the abbreviation descriptions mentioned in this paper. To gain a better understanding of the TSA, several research questions are raised as follows. **RQ1**: What is the major difference between sentiment analysis and opinion mining? Sentiment Analysis (SA) and Opinion Mining (OM) are two promising fields of study that are both employed to learn about the feelings and opinions of people regarding certain topics.