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.

翻译：社交媒体平台正逐渐成为包括信息传递与观点表达在内的社会互动基础。鉴于此，情感分析技术致力于提供解决方案，以确保对生成数据（包括情感、情绪及讨论主题）进行检索与分析。国际语义评测研讨会（SemEval）等国际竞赛吸引了众多对构建情感分析系统具有特殊研究兴趣的研究者与实践者。本研究系统考察了2013至2021年间各届SemEval竞赛的优胜系统——该时期共有658支队伍参赛，且参与热度逐年攀升。我们通过分析参评系统，揭示了研究趋势的演进脉络，重点关注情感分析系统的核心构成要素：数据获取、预处理与分类。研究表明：预处理技术得到持续应用；特征工程与词表征方法经历了从基于词典的方法到词嵌入的演进；神经网络与Transformer模型在分类阶段占据主导地位，推动了即用型模型的广泛使用。此外，我们基于已实验系统的经验为研究者提供洞见，这将助力新系统的快速原型构建，并为实践者参与未来SemEval竞赛提供参考。