  Sentiment analysis in software engineering (SE) has shown promise to analyze
and support diverse development activities. We report the results of an
empirical study that we conducted to determine the feasibility of developing an
ensemble engine by combining the polarity labels of stand-alone SE-specific
sentiment detectors. Our study has two phases. In the first phase, we pick five
SE-specific sentiment detection tools from two recently published papers by Lin
et al. [31, 32], who first reported negative results with standalone sentiment
detectors and then proposed an improved SE-specific sentiment detector, POME
[31]. We report the study results on 17,581 units (sentences/documents) coming
from six currently available sentiment benchmarks for SE. We find that the
existing tools can be complementary to each other in 85-95% of the cases, i.e.,
one is wrong, but another is right. However, a majority voting-based ensemble
of those tools fails to improve the accuracy of sentiment detection. We develop
Sentisead, a supervised tool by combining the polarity labels and bag of words
as features. Sentisead improves the performance (F1-score) of the individual
tools by 4% (over Senti4SD [5]) - 100% (over POME [31]). In a second phase, we
compare and improve Sentisead infrastructure using Pre-trained Transformer
Models (PTMs). We find that a Sentisead infrastructure with RoBERTa as the
ensemble of the five stand-alone rule-based and shallow learning SE-specific
tools from Lin et al. [31, 32] offers the best F1-score of 0.805 across the six
datasets, while a stand-alone RoBERTa shows an F1-score of 0.801.
