Toward advice mining

Abstract

Web forums are platforms for personal communications on sharing information with others. Such information is often expressed in the form of advice. In this paper, we address the problem of advice-revealing text unit (ATU) extraction from online forums due to its usefulness in travel domain. We represent advice as a two-tuple comprising an advicerevealing sentence and its context sentences. To extract the advice-revealing sentences, we propose to define the task as a sequence labeling problem, using three different types of features: syntactic, contextual, and semantic features. To extract the context sentences, we propose to use a 2 Dimensional CRF (2D-CRF) model, which gives the best performance compared to traditional machine learning models. Finally, we present a solution to the integrated problem of extracting both advice-revealing sentences and their respective context sentences at the same time using our proposed models, i.e., Multiple Linear CRF (ML-CRF) and 2 Dimensional CRF Plus (2D-CRF+). The experimental results show that ML-CRF performs better than any other models studied in this paper for extracting advice-revealing sentences and context sentences.

Opinion Extraction on Online Malay Text

“…Opinion extraction supports various tasks such as sentiment analysis of reviews, document classification and insight summarization (Wicaksono and Myaeng, 2013). The task of extracting opinions at feature level is very challenging, however it has proven to be beneficial in providing detailed insights on products.…”