@inproceedings{guo-etal-2019-personalized,
    title = "A Personalized Sentiment Model with Textual and Contextual Information",
    author = {Guo, Siwen  and
      H{\"o}hn, Sviatlana  and
      Schommer, Christoph},
    editor = "Bansal, Mohit  and
      Villavicencio, Aline",
    booktitle = "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/K19-1093/",
    doi = "10.18653/v1/K19-1093",
    pages = "992--1001",
    abstract = "In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person`s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person`s past expressions, and offer a better understanding of the sentiment from the expresser`s perspective. Additionally, we investigate how a person`s sentiment changes over time so that recent incidents or opinions may have more effect on the person`s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information."
}