10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches
Philipe F. Melo, Daniel H. Dalip, Manoel M. Junior, Marcos A., Gon\c{c}alves, Fabr\'icio Benevenuto

TL;DR
This paper introduces 10SENT, a stable, unsupervised ensemble sentiment analysis method that combines multiple approaches to improve accuracy and consistency across diverse social media datasets without manual labeling.
Contribution
It proposes a novel bootstrapped combination of popular sentiment methods, enhancing stability and effectiveness across various domains without supervised training.
Findings
10SENT outperforms individual methods in multiple datasets
The approach maintains high accuracy without manual labeling
Transfer learning further improves results
Abstract
Sentiment analysis has become a very important tool for analysis of social media data. There are several methods developed for this research field, many of them working very differently from each other, covering distinct aspects of the problem and disparate strategies. Despite the large number of existent techniques, there is no single one which fits well in all cases or for all data sources. Supervised approaches may be able to adapt to specific situations but they require manually labeled training, which is very cumbersome and expensive to acquire, mainly for a new application. In this context, in here, we propose to combine several very popular and effective state-of-the-practice sentiment analysis methods, by means of an unsupervised bootstrapped strategy for polarity classification. One of our main goals is to reduce the large variability (lack of stability) of the unsupervised…
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