# Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches

arXiv: https://arxiv.org/abs/2503.10457 · HTML: https://mlsanity.com/paper/2503.10457/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 103 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [A Convolutional Neural Network for Modelling Sentences](https://mlsanity.com/paper/1404.2188/) (2014)
- [Directions in Abusive Language Training Data: Garbage In, Garbage Out](https://mlsanity.com/paper/2004.01670/) (2020)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [Grid Search, Random Search, Genetic Algorithm: A Big Comparison for NAS](https://mlsanity.com/paper/1912.06059/) (2019)
- [SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification](https://mlsanity.com/paper/2004.14454/) (2020)
- [Is Neuro-Symbolic AI Meeting its Promise in Natural Language Processing? A Structured Review](https://mlsanity.com/paper/2202.12205/) (2022)
- [NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets](https://mlsanity.com/paper/1308.6242/) (2013)
- [NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning](https://mlsanity.com/paper/1804.06658/) (2018)
- [SemEval-2013 Task 2: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.06806/) (2019)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)
- [SeerNet at SemEval-2018 Task 1: Domain Adaptation for Affect in Tweets](https://mlsanity.com/paper/1804.06137/) (2018)
- [UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs](https://mlsanity.com/paper/1904.03450/) (2019)
- [GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection](https://mlsanity.com/paper/2007.14477/) (2020)
- [PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and #hashtags](https://mlsanity.com/paper/1804.08280/) (2018)
- [Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter](https://mlsanity.com/paper/1705.01306/) (2017)
- [MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training](https://mlsanity.com/paper/2104.10336/) (2021)

## Abstract (arXiv)

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.