Sentiment and Emotion based Text Representation for Fake Reviews Detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Fake reviews are increasingly prevalent across the Internet. They can be unethical and harmful. They can affect businesses and mislead customers. As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical. In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection. The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.
Original languageEnglish
Title of host publicationRANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING
Pages75-757
Number of pages7
Publication statusPublished - 01 Sep 2019

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Learning systems
Internet
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Cite this

Melleng, A., Jurek-Loughrey, A., & Padmanabhan, D. (2019). Sentiment and Emotion based Text Representation for Fake Reviews Detection. In RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING (pp. 75-757)
@inproceedings{a8f7391984254f36b44715a6cc4c139d,
title = "Sentiment and Emotion based Text Representation for Fake Reviews Detection",
abstract = "Fake reviews are increasingly prevalent across the Internet. They can be unethical and harmful. They can affect businesses and mislead customers. As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical. In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection. The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.",
author = "Alimuddin Melleng and Anna Jurek-Loughrey and Deepak Padmanabhan",
year = "2019",
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booktitle = "RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING",

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Melleng, A, Jurek-Loughrey, A & Padmanabhan, D 2019, Sentiment and Emotion based Text Representation for Fake Reviews Detection. in RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING. pp. 75-757.

Sentiment and Emotion based Text Representation for Fake Reviews Detection. / Melleng, Alimuddin; Jurek-Loughrey, Anna; Padmanabhan, Deepak.

RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING. 2019. p. 75-757.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Sentiment and Emotion based Text Representation for Fake Reviews Detection

AU - Melleng, Alimuddin

AU - Jurek-Loughrey, Anna

AU - Padmanabhan, Deepak

PY - 2019/9/1

Y1 - 2019/9/1

N2 - Fake reviews are increasingly prevalent across the Internet. They can be unethical and harmful. They can affect businesses and mislead customers. As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical. In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection. The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.

AB - Fake reviews are increasingly prevalent across the Internet. They can be unethical and harmful. They can affect businesses and mislead customers. As opinions on the Web are increasingly relied on, the detection of fake reviews has become more critical. In this study we explore the effectiveness of sentiment and emotions based representations for the task of building machine learning models for fake reviews detection. The experiment performed with three real-world datasets demonstrate that improved data representation can be achieved by combining sentiment and emotion extraction methods, as well as by performing sentiment and emotion analysis on a part-by-part basis by segmenting the reviews.

M3 - Conference contribution

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EP - 757

BT - RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING

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Melleng A, Jurek-Loughrey A, Padmanabhan D. Sentiment and Emotion based Text Representation for Fake Reviews Detection. In RANLP 2019 RECENT ADVANCES IN NATURAL LANGUAGE PROCESSING. 2019. p. 75-757