A systematic review of social media-based sentiment analysis: Emerging trends and challenges

In the present information age, a wide and significant variety of social media platforms have been developed and become an important part of modern life. Massive amounts of user-generated data sourced from various social networking platforms also provide new insights for businesses and governments. However, it has become difficult to extract useful information from the vast amount of information effectively. Sentiment analysis provides an automated method of analyzing sentiment, emotion and opinion in written language to address this issue. In the existing literature, a large number of scholars have worked on improving the performance of various sentiment classifiers or applying them to various domains using data from social networking platforms. This paper explores the challenges that scholars have encountered and other potential problems in studying sentiment analysis in social media. It gives insights into the goals of the sentiment analysis task, the implementation process, and the ways in which it is utilized in various application domains. It also provides a comparison of different studies and highlights several challenges related to the datasets, text languages, analysis methods and evaluation metrics. The paper contributes to the research on sentiment analysis and can help practitioners select a suitable methodology for their applications.