19 research outputs found
SEntiMoji: An Emoji-Powered Learning Approach for Sentiment Analysis in Software Engineering
Sentiment analysis has various application scenarios in software engineering
(SE), such as detecting developers' emotions in commit messages and identifying
their opinions on Q&A forums. However, commonly used out-of-the-box sentiment
analysis tools cannot obtain reliable results on SE tasks and the
misunderstanding of technical jargon is demonstrated to be the main reason.
Then, researchers have to utilize labeled SE-related texts to customize
sentiment analysis for SE tasks via a variety of algorithms. However, the
scarce labeled data can cover only very limited expressions and thus cannot
guarantee the analysis quality. To address such a problem, we turn to the
easily available emoji usage data for help. More specifically, we employ
emotional emojis as noisy labels of sentiments and propose a representation
learning approach that uses both Tweets and GitHub posts containing emojis to
learn sentiment-aware representations for SE-related texts. These emoji-labeled
posts can not only supply the technical jargon, but also incorporate more
general sentiment patterns shared across domains. They as well as labeled data
are used to learn the final sentiment classifier. Compared to the existing
sentiment analysis methods used in SE, the proposed approach can achieve
significant improvement on representative benchmark datasets. By further
contrast experiments, we find that the Tweets make a key contribution to the
power of our approach. This finding informs future research not to unilaterally
pursue the domain-specific resource, but try to transform knowledge from the
open domain through ubiquitous signals such as emojis.Comment: Accepted by the 2019 ACM Joint European Software Engineering
Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE
2019). Please include ESEC/FSE in any citation
Efficient unsupervised discovery of word categories using symmetric patterns and high frequency words
We present a novel approach for discovering word categories, sets of words sharing a significant aspect of their meaning. We utilize meta-patterns of highfrequency words and content words in order to discover pattern candidates. Symmetric patterns are then identified using graph-based measures, and word categories are created based on graph clique sets. Our method is the first pattern-based method that requires no corpus annotation or manually provided seed patterns or words. We evaluate our algorithm on very large corpora in two languages, using both human judgments and WordNet-based evaluation. Our fully unsupervised results are superior to previous work that used a POS tagged corpus, and computation time for huge corpora are orders of magnitude faster than previously reported