摘要
The connected society we live in today has allowed online users to willingly share opinions on an unprecedented scale. Motivated by the advent of mass opinion sharing, it is then crucial to devise algorithms that efficiently identify the emotions expressed within the opinionated content. Traditional opinion-based classifiers require extracting high-dimensional feature representations, which become computationally expensive to process and can misrepresent or deteriorate the accuracy of a classifier. In this paper, we propose an unsupervised graph-based approach for extracting Twitter-specific emotion-bearing patterns to be used as features. By utilizing a more representative list of patterns, as features, we improved the precision and recall of a given emotion classification task. Due to its novel bootstrapping process, the full system is also adaptable to different domains and languages. The experimented results demonstrate that the extracted patterns are effective in identifying emotions for English, Spanish, and French Twitter streams. We also provide detailed experiments and offer an extended version of our algorithm to support the classification of Indonesian microblog posts. Overall, our empirical experimented results demonstrate that the proposed approach bears desirable characteristics such as accuracy, generality, adaptability, minimal supervision, and coverage.