Leveraging statistical information in fine-grained financial sentiment analysis

Han ZHANG, Zongxi LI*, Haoran XIE, Raymond Y. K. LAU, Gary CHENG, Qing LI, Dian ZHANG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

11 Citations (Scopus)


The recent development of deep learning-based natural language processing (NLP) methods has fostered many downstream applications in various fields. As one of the applications in the financial industry, fine-grained financial sentiment analysis (FSA) aims to understand the sentimental orientation, i.e., bullish or bearish, of financial texts by predicting the polarity score and has been widely applied in the financial industry stock-related opinion mining. Because of the lack of a large-scale labeled dataset and the domain-dependent nature, FSA is challenging. Previous works mainly focus on constructing and exploiting handcrafted lexicons that encode expert knowledge to enhance the semantic features in decision making, which yields improvements but are expensive to acquire. This paper proposes a lightweight regression model incorporating the statistical distribution of a term over the polarity range, say between − 1 and 1, to address the fine-grained FSA task. More concretely, we first count each word’s appearance at different polarity intervals and produce a statistic-based representation for each text, which will be encoded as a corpus-level statistical feature vector by an autoencoder. Subsequently, the obtained feature vector will be integrated with the semantic feature vector in the regression model. Our experiments show such a model can produce significant improvements compared with the baseline models on two FSA subsets, i.e., news headlines and microblogs, without a computational overhead. Furthermore, we notice the signs that lexicon-based approaches have neglected can play an important role in FSA.
Original languageEnglish
Pages (from-to)513-531
Number of pages19
JournalWorld Wide Web
Issue number2
Early online date5 Feb 2022
Publication statusPublished - Mar 2022

Bibliographical note

This article belongs to the Topical Collection: Special Issue on Web Intelligence =Artificial Intelligence in the Connected World Guest Editors: Yuefeng Li, Amit Sheth, Athena Vakali, and Xiaohui Tao

The work of this paper has been supported by the Hong Kong Research Grants Council under the General Research Fund (Project No. 15200021), the Lam Woo Research Fund (Project No. LWI20011) and the Faculty Research Grant (Project No. DB21B6) of Lingnan University, Hong Kong, the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (Project No. MIT02/19-20) and the Research Cluster Fund (Project No. RG 78/2019-2020R) of The Education University of Hong Kong. Lau’s work was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. CityU 11507219). Dian Zhang’s work was supported by NSFC (Project No. 61872247).

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.


  • Financial sentiment analysis
  • Information retrieval
  • Natural language processing
  • Sentiment analysis


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