Year: 2014
Type: article
Abstract: Sentiment prediction techniques are often used to assign numerical scores to free‐text format reviews written by people in online review websites. In order to exploit the fine‐grained structural information of textual content, a review may be considered as a collection of sentences, each with its own sentiment orientation and score. In this manner, a score aggregation method is needed to combine sentence‐level scores into an overall review rating. While recent work has concentrated on designing effective sentence‐level prediction methods, there remains the problem of finding efficient algorithms for score aggregation. In this study, we investigate different aggregation methods, as well as the cases in which they perform poorly. According to the analysis of existing methods, we propose a new score aggregation method based on the Dempster-Shafer theory of evidence. In the proposed method, we first detect the polarity of reviews using a machine learning approach and then, consider sentence scores as evidence for the overall review rating. The results from two public social web datasets show the higher performance of our method in comparison with existing score aggregation methods and state‐of‐the‐art machine learning approaches.
DOI: 10.1155/2014/361201
Institution: University of Isfahan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,550
Award: G5323433010
OpenAlex ID: W2081031258