SentiStream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams

Yuhao Wu, Karthick Sharma, Chun Wei Seah, Shuhao Zhang

Submission Type: Regular Long Paper

Submission Track: Sentiment Analysis, Stylistic Analysis, and Argument Mining

Submission Track 2: Efficient Methods for NLP

Keywords: Online Sentiment analysis, Streaming learning

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.

Submission Number: 1329