SentiMLBench: Benchmark Evaluation of Machine Learning Algorithms for Sentiment Analysis

Abstract

Sentiment Analysis has been a topic of interest for researchers due to its increasing usage by Industry. To measure end-user sentiment., there is no clear verdict on which algorithms are better in real-time scenarios. A rigorous benchmark evaluation of various algorithms running across multiple datasets and different hardware architectures is required that can guide future researchers on potential advantages and limitations. In this paper, proposed SentiMLBench is a critical evaluation of key ML algorithms as standalone classifiers, a novel cascade feature selection (CFS) based ensemble technique in multiple benchmark environments each using a different twitter dataset and processing hardware. The best trained ensemble model with CFS enhancement surpasses current state-of-the-art models, according to experimental results. In a study, though ensemble model provides good accuracy, it falls short of neural networks accuracy by 2%. ML algorithms accuracy is poor as standalone classifiers across all three studies. The supremacy of neural networks is further stamped in study three where it outperforms other algorithms in accuracy by over 10%. Graphical processing unit provide speed and higher computational power at a fraction of a cost compared to a normal processor thereby providing critical architectural insights into developing a robust expert system for sentiment analysis.

Sentiment analysis using naive bayes for reviews of visitors to Padang City beach tourism after the COVID-19 pandemic

“…Sentiment analysis is a computerized technology that can help and analyze a textual opinion sentence which works by understanding and extracting it like text mining to produce sentiment information [16][17][18]. Sentiment analysis, also called opinion mining, is a field of study that analyzes opinions, sentiments, evaluations, judgments, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes [19]. It represents a large problem space.…”