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This paper evaluates the effectiveness and computational efficiency of four lexicon-based sentiment analysis methods: AFINN, VADER, TextBlob, and SentiWordNet, on a resource-limited target device. We assessed each method’s sentiment classification accuracy, precision, recall, F1 score, memory consumption, and processing time on a limited annotated dataset. TextBlob performed well, achieving the highest accuracy and low resource usage among the candidates, whereas SentiWordNet consistently lagged across all metrics. We discuss performance-resource trade-offs for deploying sentiment analysis in low-compute applications where it is essential to efficiently manage local data processing and resources.

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Evaluating Lexicon-Based Sentiment Analysis Methods for Small Datasets on Low Compute Devices

  • 会议论文

  • 发表日期：

    2025年

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  • Scott Johnson,
  • Farnaz Baksh,
  • Matevž Borjan Zorec

摘要

This paper evaluates the effectiveness and computational efficiency of four lexicon-based sentiment analysis methods: AFINN, VADER, TextBlob, and SentiWordNet, on a resource-limited target device. We assessed each method’s sentiment classification accuracy, precision, recall, F1 score, memory consumption, and processing time on a limited annotated dataset. TextBlob performed well, achieving the highest accuracy and low resource usage among the candidates, whereas SentiWordNet consistently lagged across all metrics. We discuss performance-resource trade-offs for deploying sentiment analysis in low-compute applications where it is essential to efficiently manage local data processing and resources.