@inproceedings{wu-etal-2023-sentistream,
title = "{S}enti{S}tream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams",
author = "Wu, Yuhao and
Sharma, Karthick and
Seah, Chun and
Zhang, Shuhao",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.380",
doi = "10.18653/v1/2023.emnlp-main.380",
pages = "6198--6212",
abstract = "Online sentiment analysis has emerged as a crucial component in numerous data-driven applications, including social media monitoring, customer feedback analysis, and online reputation management. Despite their importance, current methodologies falter in effectively managing the continuously evolving nature of data streams, largely due to their reliance on substantial, pre-existing labelled datasets. This paper presents $\textbf{sentistream}$, a novel co-training framework specifically designed for efficient sentiment analysis within dynamic data streams. Comprising unsupervised, semi-supervised, and stream merge modules, $\textbf{ sentistream}$ guarantees constant adaptability to evolving data landscapes. This research delves into the continuous adaptation of language models for online sentiment analysis, focusing on real-world applications. Experimental evaluations using data streams derived from three benchmark sentiment analysis datasets confirm that our proposed methodology surpasses existing approaches in terms of both accuracy and computational efficiency.",
}
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<abstract>Online sentiment analysis has emerged as a crucial component in numerous data-driven applications, including social media monitoring, customer feedback analysis, and online reputation management. Despite their importance, current methodologies falter in effectively managing the continuously evolving nature of data streams, largely due to their reliance on substantial, pre-existing labelled datasets. This paper presents sentistream, a novel co-training framework specifically designed for efficient sentiment analysis within dynamic data streams. Comprising unsupervised, semi-supervised, and stream merge modules, sentistream guarantees constant adaptability to evolving data landscapes. This research delves into the continuous adaptation of language models for online sentiment analysis, focusing on real-world applications. Experimental evaluations using data streams derived from three benchmark sentiment analysis datasets confirm that our proposed methodology surpasses existing approaches in terms of both accuracy and computational efficiency.</abstract>
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%0 Conference Proceedings
%T SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams
%A Wu, Yuhao
%A Sharma, Karthick
%A Seah, Chun
%A Zhang, Shuhao
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wu-etal-2023-sentistream
%X Online sentiment analysis has emerged as a crucial component in numerous data-driven applications, including social media monitoring, customer feedback analysis, and online reputation management. Despite their importance, current methodologies falter in effectively managing the continuously evolving nature of data streams, largely due to their reliance on substantial, pre-existing labelled datasets. This paper presents sentistream, a novel co-training framework specifically designed for efficient sentiment analysis within dynamic data streams. Comprising unsupervised, semi-supervised, and stream merge modules, sentistream guarantees constant adaptability to evolving data landscapes. This research delves into the continuous adaptation of language models for online sentiment analysis, focusing on real-world applications. Experimental evaluations using data streams derived from three benchmark sentiment analysis datasets confirm that our proposed methodology surpasses existing approaches in terms of both accuracy and computational efficiency.
%R 10.18653/v1/2023.emnlp-main.380
%U https://aclanthology.org/2023.emnlp-main.380
%U https://doi.org/10.18653/v1/2023.emnlp-main.380
%P 6198-6212
Markdown (Informal)
[SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams](https://aclanthology.org/2023.emnlp-main.380) (Wu et al., EMNLP 2023)
ACL