# How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies

arXiv: https://arxiv.org/abs/2104.10513 · HTML: https://mlsanity.com/paper/2104.10513/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [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)
- [BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs](https://mlsanity.com/paper/1704.06125/) (2017)
- [SemEval-2015 Task 10: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02387/) (2019)

## Built on by (most cited first, 3 of 3)

- [M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets](https://mlsanity.com/paper/2404.01753/) (2024)
- [Sentiment Analysis of Movie Reviews Using BERT](https://mlsanity.com/paper/2502.18841/) (2025)
- [Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis](https://mlsanity.com/paper/2502.20682/) (2025)

## Abstract (arXiv)

Twitter sentiment analysis, which often focuses on predicting the polarity of tweets, has attracted increasing attention over the last years, in particular with the rise of deep learning (DL). In this paper, we propose a new task: predicting the predominant sentiment among (first-order) replies to a given tweet. Therefore, we created RETWEET, a large dataset of tweets and replies manually annotated with sentiment labels. As a strong baseline, we propose a two-stage DL-based method: first, we create automatically labeled training data by applying a standard sentiment classifier to tweet replies and aggregating its predictions for each original tweet; our rationale is that individual errors made by the classifier are likely to cancel out in the aggregation step. Second, we use the automatically labeled data for supervised training of a neural network to predict reply sentiment from the original tweets. The resulting classifier is evaluated on the new RETWEET dataset, showing promising results, especially considering that it has been trained without any manually labeled data. Both the dataset and the baseline implementation are publicly available.