Search for: All records

Aspect classification, identifying aspects of text segments, facilitates numerous applications, such as sentiment analysis and review summarization. To alleviate the extensive human effort required by existing aspect classification methods, in this paper, we focus on a weakly supervised setting—the model input only contains domainspecific raw texts and a few seed words per pre-defined aspect. We identify a unique challenge here as to how to classify texts without any pre-defined aspects. The existence of this kind of “misc” aspect text segments is very common in review corpora. It is difficult, even for domain experts, to nominate seed words for the “misc” aspect, which makes existing seed-driven text classification methods not applicable. Therefore, we propose to jointly model pre-defined aspects and the “misc” aspect through a novel framework, ARYA. It enables mutual enhancements between pre-defined aspects and the “misc” aspect via iterative classifier training and seed set updating. Specifically, it trains a classifier for pre-defined aspects and then leverages it to induce the supervision for the “misc” aspect. The prediction results of the “misc” aspect are later utilized to further filter the seed word selections for pre-defined aspects. Experiments in three domains demonstrate the superior performance of our proposed framework, as well as the necessity and importance of properly modeling the “misc” aspect