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Multi-Entity Polarity Analysis in Financial Documents

Published: 18 November 2014 Publication History

Abstract

The amount of information available in the Internet does not allow performing manual content analysis to identify information of interest. Thus automated analyses are used to identify information of interest, and one increasingly important approach is the polarity analysis. Polarity analysis is the classification of a text document in positive, negative, and neutral, according to a certain topic. This classification of information is particularly useful in the finance domain, where news about a company can affect the performance of its stocks. Although most of the methods in financial domain consider that the whole document is associated with a particular entity, this is not always the case. In fact, it is common that authors cite several entities in a single document and these entities are cited with different polarity. Accordingly, the objective of this paper was to study strategies for polarity detection in financial documents with multiple entities. Specifically, we studied methods based on learning of multiple models, one for each observed entity, using SVM classifiers. We evaluated models based on the partition of documents into fragments according to the entities they cite. We used several heuristics to segment documents based on shallow and deep natural language processing (NLP). We found that entity-specific models created by partitioning the document collection into segments outperformed the strategy based on the use of entire documents. We also observed that more complex segmentation using anaphora resolution was not able to outperform a low-cost approach, based on simple string matching.

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Cited By

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  • (2024)Summarizing Charts of Financial Document via Context-Aware Multi-Modeling2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651528(1-8)Online publication date: 30-Jun-2024
  • (2021)Machine Learning and FinanceInternational Journal for Innovation Education and Research10.31686/ijier.vol9.iss4.30169:4(29-55)Online publication date: 1-Apr-2021
  • (2018)A Study On The Use of Deep Learning for Automatic Audience CountingProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243110(221-228)Online publication date: 16-Oct-2018

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  1. Multi-Entity Polarity Analysis in Financial Documents

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      cover image ACM Other conferences
      WebMedia '14: Proceedings of the 20th Brazilian Symposium on Multimedia and the Web
      November 2014
      256 pages
      ISBN:9781450332309
      DOI:10.1145/2664551
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Published: 18 November 2014

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      Author Tags

      1. anaphora resolution
      2. document engineering
      3. machine learning
      4. sentiment analysis
      5. web data annotation

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      WebMedia'14
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      • SBC
      WebMedia'14: 20th Brazilian Symposium on Multimedia and the Web
      November 18 - 21, 2014
      João Pessoa, Brazil

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      WebMedia '14 Paper Acceptance Rate 25 of 86 submissions, 29%;
      Overall Acceptance Rate 270 of 873 submissions, 31%

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      View all
      • (2024)Summarizing Charts of Financial Document via Context-Aware Multi-Modeling2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651528(1-8)Online publication date: 30-Jun-2024
      • (2021)Machine Learning and FinanceInternational Journal for Innovation Education and Research10.31686/ijier.vol9.iss4.30169:4(29-55)Online publication date: 1-Apr-2021
      • (2018)A Study On The Use of Deep Learning for Automatic Audience CountingProceedings of the 24th Brazilian Symposium on Multimedia and the Web10.1145/3243082.3243110(221-228)Online publication date: 16-Oct-2018
      • (undefined)Machine Learning in Finance: A Topic Modeling ApproachSSRN Electronic Journal10.2139/ssrn.3327277

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