Sentiment analysis with covariate-assisted word embeddings

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

2 Scopus Citations
View graph of relations

Related Research Unit(s)


Original languageEnglish
Pages (from-to)3015-3039
Journal / PublicationElectronic Journal of Statistics
Issue number1
Online published4 Jun 2021
Publication statusPublished - 2021



Sentiment analysis measures inclination of textual documents, aiming to extract and quantify their subjective sentiment polarity. In literature, most sentiment analysis methods first numericalize textual documents through certain word embeddings framework, and then formulate sentiment analysis as an ordinal regression or classification task. Yet it is often ignored that different people may have different preference of wording, and thus a uniform word embeddings often leads to suboptimal performance. In this article, to accommodate the heterogeneity among individual persons, we propose a covariate-assisted word embeddings in a margin-based ordinal regression framework, where covariates are incorporated through scaling factors to adjust the word embeddings. Moreover, we employ a block-wise coordinate descent scheme to tackle the resultant large-scale optimization task, and establish theoretical results to quantify the asymptotic behavior of the proposed method, guaranteeing its fast convergence rate in terms of prediction accuracy. Finally, we demonstrate the advantages of the proposed method over its competitors in both the Yelp Challenge dataset and synthetic datasets.

Research Area(s)

  • Personalized prediction, sentiment analysis, word embeddings, unstructured data, ordinal regression

Download Statistics

No data available