@inproceedings{fc414ffa5ff74f4d90df891d0cf44f5c,
title = "Leveraging Personalized Sentiment Lexicons for Sentiment Analysis",
abstract = "We propose a novel personalized approach for the sentiment analysis task. The approach is based on the intuition that the same sentiment words can carry different sentiment weights for different users. For each user, we learn a language model over a sentiment lexicon to capture her writing style. We further correlate this user-specific language model with the user's historical ratings of reviews. Additionally, we discuss how two standard CNN and CNN+LSTM models can be improved by adding these user-based features. Our evaluation on the Yelp dataset shows that the proposed new personalized sentiment analysis features are effective.",
keywords = "personalization, sentiment analysis, sentiment lexicons",
author = "Dominic Seyler and Jiaming Shen and Jinfeng Xiao and Yiren Wang and Zhai, {Cheng Xiang}",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; 6th ACM SIGIR / 10th International Conference on the Theory of Information Retrieval, ICTIR 2020 ; Conference date: 14-09-2020 Through 17-09-2020",
year = "2020",
month = sep,
day = "14",
doi = "10.1145/3409256.3409850",
language = "English (US)",
series = "ICTIR 2020 - Proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval",
publisher = "Association for Computing Machinery",
pages = "109--112",
booktitle = "ICTIR 2020 - Proceedings of the 2020 ACM SIGIR International Conference on Theory of Information Retrieval",
address = "United States",
}