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

Tayebi Arasteh S, Monajem M, Christlein V, Heinrich P, Nicolao A, Boldaji HN, Lotfinia M, Evert S (2021)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Series: 2021 IEEE 15th International Conference on Semantic Computing (ICSC)

Pages Range: 370-373

Conference Proceedings Title: 2021 IEEE 15th International Conference on Semantic Computing (ICSC)

Event location: Laguna Hills, CA

ISBN: 9781728188997

URI: https://ieeexplore.ieee.org/document/9364527

DOI: 10.1109/ICSC50631.2021.00068

Abstract

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.

Authors with CRIS profile

Additional Organisation(s)

Involved external institutions

Sharif University of Technology / دانشگاه صنعتی شریف (SUT) Iran, Islamic Republic of (IR)

How to cite

APA:

Tayebi Arasteh, S., Monajem, M., Christlein, V., Heinrich, P., Nicolao, A., Boldaji, H.N.,... Evert, S. (2021). How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies. In IEEE (Eds.), 2021 IEEE 15th International Conference on Semantic Computing (ICSC) (pp. 370-373). Laguna Hills, CA, US.

MLA:

Tayebi Arasteh, Soroosh, et al. "How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies." Proceedings of the IEEE 15th International Conference on Semantic Computing (ICSC), Laguna Hills, CA Ed. IEEE, 2021. 370-373.

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