@inproceedings{c0cab22677ee48169d74d25bb2d6e8cb,
title = "Identifying helpful online reviews with word embedding features",
abstract = "The advent of Web 2.0 has enabled users to share their opinions via various social media websites. People{\textquoteright}s decision-making process is strongly influenced by online reviews. Predicting the helpfulness of reviews can help to save time and find helpful suggestions. However, most of previous works focused on exploring new features with external data source, such as user{\textquoteright}s profile, semantic dictionaries, etc. In this paper, we maintain that the helpfulness of an online review can be predicted by knowing only word embedding information. Word embedding information is a kind of word semantic representation computed with word context. We hypothesize that word embedding information would allow us to accurately predict the helpfulness of an online review. The experiments were conducted to prove this hypothesis and the results showed a substantial improvement compared with baselines of features previously used.",
keywords = "Automatic helpfulness voting, Helpfulness classification, User preference",
author = "Jie Chen and Chunxia Zhang and Zhendong Niu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.; 9th International Conference on Knowledge Science, Engineering and Management, KSEM 2016 ; Conference date: 05-10-2016 Through 07-10-2016",
year = "2016",
doi = "10.1007/978-3-319-47650-6\_10",
language = "English",
isbn = "9783319476490",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "123--133",
editor = "Franz Lehner and Nora Fteimi",
booktitle = "Knowledge Science, Engineering and Management - 9th International Conference, KSEM 2016, Proceedings",
address = "Germany",
}