# Supervised Sentiment Classification with CNNs for Diverse SE Datasets

## TL;DR

This paper introduces a CNN-LSTM hierarchical model trained on pre-trained word vectors for sentiment analysis in software engineering, significantly improving accuracy over existing tools across multiple datasets.

## Contribution

The study presents a novel supervised deep learning model tailored for SE sentiment analysis, outperforming existing methods and demonstrating the benefits of small-scale re-training.

## Key findings

- Model achieves state-of-the-art accuracy on all datasets.
- Supervised re-training with small labeled samples improves performance.
- Deep learning models outperform traditional sentiment analysis tools in SE context.

## Abstract

Sentiment analysis, a popular technique for opinion mining, has been used by the software engineering research community for tasks such as assessing app reviews, developer emotions in issue trackers and developer opinions on APIs. Past research indicates that state-of-the-art sentiment analysis techniques have poor performance on SE data. This is because sentiment analysis tools are often designed to work on non-technical documents such as movie reviews. In this study, we attempt to solve the issues with existing sentiment analysis techniques for SE texts by proposing a hierarchical model based on convolutional neural networks (CNN) and long short-term memory (LSTM) trained on top of pre-trained word vectors. We assessed our model's performance and reliability by comparing it with a number of frequently used sentiment analysis tools on five gold standard datasets. Our results show that our model pushes the state of the art further on all datasets in terms of accuracy. We also show that it is possible to get better accuracy after labelling a small sample of the dataset and re-training our model rather than using an unsupervised classifier.