S enti S tream: A Co-Training Framework for Adaptive Online Sentiment Analysis in Evolving Data Streams (2023)

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.

## introduction online sentiment analysis (osa) has established its significance in the realm of sentiment analysis, with its primary objective being the identification of polarity in ceaselessly incoming data streams @xcite . this task requires proficiency in two key aspects: online adaptation and sentiment classification. the requirement for online adaptation arises from the ever-evolving characteristics of real-time data streams, an effect commonly known as concept drift @xcite . this necessitates continuous model adaptation to maintain effectiveness. simultaneously, sentiment classification, a core task within natural language processing (nlp), has found its cruciality in a myriad of sectors such as customer feedback interpretation and public opinion monitoring @xcite . however, creating an osa approach that can simultaneously handle online adaptation and sentiment classification remains a challenging feat. previous research @xcite @xcite @xcite @xcite has indicated that supervised learning paradigms can yield high accuracy in sentiment analysis. despite their strengths, these methods frequently overlook the ceaseless accumulation of real-world streaming data originating from varied sources like literature @xcite , news articles @xcite , and scientific papers @xcite . the constant emergence of this dynamic streaming data often leads to the concept drift effect, which can impair the performance of traditional offline methods @xcite . continuous learning attempts to tackle concept drift and adapt to the ongoing data stream, but obtaining ground truth labels for this streaming data is often arduous and costly, which consequently limits the continuous application of supervised techniques and reduces their long-term efficacy. in response to these challenges, we present sentistream, a co-training framework tailored explicitly for efficient online sentiment analysis of swift-flowing opinion data. this framework consists of three modules: unsupervised, semisupervised, and stream merge. the unsupervised module uses continuously adapted pre-trained language models (plms) to distill knowledge from unlabeled streaming data, coupled with lexicon-based strategies to produce preliminary polarity labels. sentistream harnesses semantic and temporal information from text-based data streams to incrementally retrain the plms, later employing a nimble lexicon-based classification method to generate polarity labels using the updated plms. the semi-supervised module constructs a weakly supervised classification model with a small labeled dataset and continuously retrains this model with pseudo-labels generated by the stream merge module. the final stream merge module consolidates outputs from the preceding two modules, utilizing their confidence scores to dynamically update the lexicon for the unsupervised module, providing pseudo-labeled data for semi-supervised learning and dynamically fine-tuning the threshold for the semi-supervised module. we assessed the performance of sentistream on five benchmark sentiment analysis datasets and juxtaposing its efficacy against several unsupervised and semi-supervised benchmarks. the experimental results confirm that our methodology considerably outperforms existing methods in tackling dynamic data streams for online sentiment analysis tasks. additionally, sentistream uses a lightweight model, ensuring superior throughput and latency performance. the major contributions of our work can be summarized as: ## related work in this section, we provide an overview of relevant literature on online sentiment analysis, continual learning, and semi-supervised learning, thereby laying the foundation for our proposed framework. ## proposed methodology our proposed method, sentistream, consists of two parallel sentiment classification modules and a shared output co-trainer: an unsupervised module, a semi-supervised module, and a stream merge module. the overall structure is shown in figure 1 . problem formulation: our focus is on effectively conducting sentiment analysis of streaming opinion data in real-time. we define the term input stream as a sequence of tuples, referred to as @xmath0, ..., t n , which arrive at our system in chronological order. each tuple, denoted as t , consists of a finite number of sentences, x i , forming @xmath1. the sentiment polarity is either positive or negative. our goal with sentistream is to learn and identify the polarity of t (x 1 ∼ x m ) ∈ s as soon as t i arrives. ## experimental evaluation in this section, we study the performance of the different algorithms on three datasets, compare them with different baselines, and discuss the qualitative analysis of our model's performance. ## acknowledgement we would like to thank the anonymous reviewers, our meta-reviewer, and senior area chairs for their insightful comments and support with this work. this project is supported by tl@sutd seed research project grant (rtdss2214051) and the national research foundation, singapore and infocomm media development authority under its future communications research & development programme fcp-sutd-rg-2022-006. the computational work for this article was partially performed on resources of the national supercomputing centre (nscc), singapore ( https://www.nscc.sg ).