On the Limitations of Combining Sentiment Analysis Tools in a Cross-Platform Setting

TL;DR

This paper investigates the effectiveness of combining multiple sentiment analysis tools in cross-platform settings, revealing that such combinations often underperform compared to the best individual tool due to domain and data set differences.

Contribution

It provides an empirical analysis of combining sentiment analysis tools across different domains, highlighting limitations and suggesting that individual tools may outperform ensembles in cross-platform scenarios.

Findings

Ensemble methods work well within the same platform.

Majority voting does not improve results across different platforms.

The best individual tool often outperforms combined approaches in cross-platform settings.

Abstract

A positive working climate is essential in modern software development. It enhances productivity since a satisfied developer tends to deliver better results. Sentiment analysis tools are a means to analyze and classify textual communication between developers according to the polarity of the statements. Most of these tools deliver promising results when used with test data from the domain they are developed for (e.g., GitHub). But the tools' outcomes lack reliability when used in a different domain (e.g., Stack Overflow). One possible way to mitigate this problem is to combine different tools trained in different domains. In this paper, we analyze a combination of three sentiment analysis tools in a voting classifier according to their reliability and performance. The tools are trained and evaluated using five already existing polarity data sets (e.g. from GitHub). The results indicate that this kind of combination of tools is a good choice in the within-platform setting. However, a majority vote does not necessarily lead to better results when applying in cross-platform domains. In most cases, the best individual tool in the ensemble is preferable. This is mainly due to the often large difference in performance of the individual tools, even on the same data set. However, this may also be due to the different annotated data sets.