Fraud Detection of Facebook Business Page Based on Sentiment Analysis

Proceedings of International Joint Conference on Computational Intelligence - Algorithms for Intelligent Systems ◽

10.1007/978-981-13-7564-4_25 ◽

2019 ◽

pp. 279-287

Author(s):

Samia Nasrin ◽

Priyanka Ghosh ◽

S. M. Mazharul Hoque Chowdhury ◽

Sheikh Abujar ◽

Syed Akhter Hossain

Keyword(s):

Sentiment Analysis ◽

Fraud Detection

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Accounting Fraud Detection Using Forensic Techniques Based on Sentiment Analysis and Interpretable Machine Learning : Focused on Internal Control over Financial Reporting

Korean Accounting Review ◽

10.24056/kar.2021.12.006 ◽

2021 ◽

Vol 46 (6) ◽

pp. 181-218

Author(s):

Woojune Jung ◽

Jaewoon Yoon ◽

Kyungho Kim

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Financial Reporting ◽

Internal Control ◽

Fraud Detection ◽

Accounting Fraud ◽

Interpretable Machine Learning ◽

Learning Focused

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Study of Sentiment of Governor's Election Opinion in 2018

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841124 ◽

2018 ◽

pp. 231-238

Author(s):

Agung Eddy Suryo Saputro ◽

Khairil Anwar Notodiputro ◽

Indahwati A

Keyword(s):

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Addition Method ◽

Sentiment Mining ◽

Positive Sentiment ◽

An historical analysis of species references in American English

Corpora ◽

10.3366/cor.2019.0177 ◽

2019 ◽

Vol 14 (3) ◽

pp. 327-349

Author(s):

Craig Frayne

Keyword(s):

Environmental Change ◽

Sentiment Analysis ◽

Quantitative Methods ◽

English Language ◽

Language Use ◽

American English ◽

Historical Analysis ◽

The Past ◽

Corpus Studies ◽

Google Books

This study uses the two largest available American English language corpora, Google Books and the Corpus of Historical American English (coha), to investigate relations between ecology and language. The paper introduces ecolinguistics as a promising theme for corpus research. While some previous ecolinguistic research has used corpus approaches, there is a case to be made for quantitative methods that draw on larger datasets. Building on other corpus studies that have made connections between language use and environmental change, this paper investigates whether linguistic references to other species have changed in the past two centuries and, if so, how. The methodology consists of two main parts: an examination of the frequency of common names of species followed by aspect-level sentiment analysis of concordance lines. Results point to both opportunities and challenges associated with applying corpus methods to ecolinguistc research.

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