Complaint and Severity Identification From Online Financial Content

The automatic detection of financial complaints ( xmlns:xlink="http://www.w3.org/1999/xlink">FINCORP ) can benefit businesses and online merchants. Compared with manually tagged complaints, they use this information to monitor address issues effectively route them appropriate teams. This also promote greater transparency accountability when dealing consumer financial products services, strengthening the firm’s brand value. In linguistic studies, complaints have been classified into severity categories based on level risk complainant is prepared accept. Furthermore, since emotions influence every speech act, an individual’s emotional state considerably impacts complaint expression. article, we introduce a resource, collection annotated arising between institutions consumers expressed in English Twitter. dataset has enriched associated emotion, sentiment, classes. comprises 3149 3133 noncomplaint instances spanning over ten domains (e.g., credit cards, mortgages). For comprehensive evaluation our dataset, develop multitask framework for classification emotion recognition (ER) sentiment as additional tasks compare it several existing baselines. corpus code are available here: https://github.com/RohanBh23/FINCORP.