Paper #2

A Systematic Literature Review on Cross Domain Sentiment Analysis Techniques: PRISMA Approach

Rati Sharma and Kamlesh Lakhwani

Abstract: Cross Domain Sentiment Analysis (CDSA) is a method that uses rich and quality-labeled source domain data to identify the sentiments of poorly or without labeled target data. In the past decade, ample research studies have focused on this topic to solve and propose efficient CDSA methods. However, an extensive investigation of these past studies is required to find a window of improvement. The main aim of the study is to figure out considerable developments, methodologies, and SOTA techniques in the recent past. This research study presents a systematic literature review to analyze the CDSA studies published from 2017 to 2023. The authors have selected 34 articles overall and categorized them into seven different CSDA techniques. The extensive analysis of these studies’ results (in the form of graphs and tables) into different parameters that impact the performance of the CDSA. The survey finds out that major research studies tried to create a relationship between pivots and non-pivots to gain accuracy. This paper presents a synthesized review of CDSA and explores the current methods and potential future directions. It also addresses the challenges and opportunities presented by these emerging trends and their significance for researchers and practitioners in the CDSA field.

Keywords: Cross Domain Sentiment Analysis; Domain Adaptation; PRISMA 2020; Transfer Learning.