Exploiting the Unique Expression for Improved Sentiment Analysis in Software Engineering Text
This work addresses the challenge of accurately analyzing sentiments in software engineering texts, which is crucial for tasks like app review evaluation, but it is incremental as it builds on existing methods by introducing a new perspective.
The paper tackled the problem of unreliable sentiment analysis in software engineering texts by proposing a method that uses sentence structures to identify and adjust sentiment expressions, achieving improved performance over dictionary-based baselines in an empirical evaluation on four datasets.
Sentiment analysis on software engineering (SE) texts has been widely used in the SE research, such as evaluating app reviews or analyzing developers sentiments in commit messages. To better support the use of automated sentiment analysis for SE tasks, researchers built an SE-domain-specified sentiment dictionary to further improve the accuracy of the results. Unfortunately, recent work reported that current mainstream tools for sentiment analysis still cannot provide reliable results when analyzing the sentiments in SE texts. We suggest that the reason for this situation is because the way of expressing sentiments in SE texts is largely different from the way in social network or movie comments. In this paper, we propose to improve sentiment analysis in SE texts by using sentence structures, a different perspective from building a domain dictionary. Specifically, we use sentence structures to first identify whether the author is expressing her sentiment in a given clause of an SE text, and to further adjust the calculation of sentiments which are confirmed in the clause. An empirical evaluation based on four different datasets shows that our approach can outperform two dictionary-based baseline approaches, and is more generalizable compared to a learning-based baseline approach.