Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches
This review helps researchers and practitioners in natural language processing by summarizing evolution and insights for building sentiment analysis systems, but it is incremental as it compiles existing work.
The paper reviewed top-ranking sentiment analysis systems from SemEval competitions from 2013 to 2021, analyzing trends in data acquisition, preprocessing, and classification, and found a shift from lexicon-based methods to neural networks and transformers.
Social media platforms are becoming the foundations of social interactions including messaging and opinion expression. In this regard, Sentiment Analysis techniques focus on providing solutions to ensure the retrieval and analysis of generated data including sentiments, emotions, and discussed topics. International competitions such as the International Workshop on Semantic Evaluation (SemEval) have attracted many researchers and practitioners with a special research interest in building sentiment analysis systems. In our work, we study top-ranking systems for each SemEval edition during the 2013-2021 period, a total of 658 teams participated in these editions with increasing interest over years. We analyze the proposed systems marking the evolution of research trends with a focus on the main components of sentiment analysis systems including data acquisition, preprocessing, and classification. Our study shows an active use of preprocessing techniques, an evolution of features engineering and word representation from lexicon-based approaches to word embeddings, and the dominance of neural networks and transformers over the classification phase fostering the use of ready-to-use models. Moreover, we provide researchers with insights based on experimented systems which will allow rapid prototyping of new systems and help practitioners build for future SemEval editions.