# Sentiment Polarity Detection for Software Development

arXiv: https://arxiv.org/abs/1709.02984 · HTML: https://mlsanity.com/paper/1709.02984/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Sep 2017.

## Built on by (most cited first, 12 of 12)

- [Is Stack Overflow Obsolete? An Empirical Study of the Characteristics of ChatGPT Answers to Stack Overflow Questions](https://mlsanity.com/paper/2308.02312/) (2023)
- [On the validity of pre-trained transformers for natural language processing in the software engineering domain](https://mlsanity.com/paper/2109.04738/) (2021)
- [BERT-Based Sentiment Analysis: A Software Engineering Perspective](https://mlsanity.com/paper/2106.02581/) (2021)
- [Identifying Self-Admitted Technical Debt in Issue Tracking Systems using Machine Learning](https://mlsanity.com/paper/2202.02180/) (2022)
- [An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering Datasets](https://mlsanity.com/paper/2111.03196/) (2021)
- [On Technical Trading and Social Media Indicators in Cryptocurrencies' Price Classification Through Deep Learning](https://mlsanity.com/paper/2102.08189/) (2021)
- [Assessing the Use of AutoML for Data-Driven Software Engineering](https://mlsanity.com/paper/2307.10774/) (2023)
- [Using Personality Detection Tools for Software Engineering Research: How Far Can We Go?](https://mlsanity.com/paper/2110.05035/) (2021)
- [OSN Dashboard Tool For Sentiment Analysis](https://mlsanity.com/paper/2206.06935/) (2022)
- [Sentiment Analysis in Software Engineering: Evaluating Generative Pre-trained Transformers](https://mlsanity.com/paper/2505.14692/) (2025)
- [LLMs for Qualitative Data Analysis Fail on Security-specificComments in Human Experiments](https://mlsanity.com/paper/2604.10834/) (2026)
- [Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go?](https://mlsanity.com/paper/2604.13826/) (2026)

## Abstract (arXiv)

The role of sentiment analysis is increasingly emerging to study software developers' emotions by mining crowd-generated content within social software engineering tools. However, off-the-shelf sentiment analysis tools have been trained on non-technical domains and general-purpose social media, thus resulting in misclassifications of technical jargon and problem reports. Here, we present Senti4SD, a classifier specifically trained to support sentiment analysis in developers' communication channels. Senti4SD is trained and validated using a gold standard of Stack Overflow questions, answers, and comments manually annotated for sentiment polarity. It exploits a suite of both lexicon- and keyword-based features, as well as semantic features based on word embedding. With respect to a mainstream off-the-shelf tool, which we use as a baseline, Senti4SD reduces the misclassifications of neutral and positive posts as emotionally negative. To encourage replications, we release a lab package including the classifier, the word embedding space, and the gold standard with annotation guidelines.