TexRep: A Text Mining Framework for Online Reputation Monitoring

• Type: Article in International Scientific Journal
• Date: 2017

Authors

Saleiro, P (Author) Other; Rodrigues, EM (Author) Other; Carlos Soares (Author) FEUP; Eugénio Oliveira (Author) FEUP

Journal

Title: New Generation Computing

Vol. 35 No. 4

Pages: 365-389

ISSN: 0288-3635

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 9 Citations

Scopus - 9 Citations

Other information

Authenticus ID: P-00N-3QQ

DOI: 10.1007/s00354-017-0021-3

Abstract (EN): This work aims to understand, formalize and explore the scientific challenges of using unstructured text data from different Web sources for Online Reputation Monitoring. We here present TexRep, an adaptable text mining framework specifically tailored for Online Reputation Monitoring that can be reused in multiple application scenarios, from politics to finance. This framework is able to collect texts from online media, such as Twitter, and identify entities of interest and classify sentiment polarity and intensity. The framework supports multiple data aggregation methods, as well as visualization and modeling techniques that can be used for both descriptive analytics, such as analyze how political polls evolve over time, and predictive analytics, such as predict elections. We here present case studies that illustrate and validate TexRep for Online Reputation Monitoring. In particular, we provide an evaluation of TexRep Entity Filtering and Sentiment Analysis modules using well known external benchmarks. We also present an illustrative example of TexRep application in the political domain.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 25