Sentiment Classification using N-gram IDF and Automated Machine Learning
This work addresses sentiment analysis for software engineering or similar domains, but it is incremental as it integrates existing techniques without major innovation.
The authors tackled sentiment classification by combining n-gram IDF for feature extraction with automated machine learning for classifier selection, achieving the highest F1 scores for positive and negative sentences across all tested datasets.
We propose a sentiment classification method with a general machine learning framework. For feature representation, n-gram IDF is used to extract software-engineering-related, dataset-specific, positive, neutral, and negative n-gram expressions. For classifiers, an automated machine learning tool is used. In the comparison using publicly available datasets, our method achieved the highest F1 values in positive and negative sentences on all datasets.