Classification of Sentimental Reviews Using Machine Learning Techniques

Abinash Tripathy; Ankit Agrawal; Santanu Kumar Rath

doi:10.1016/j.procs.2015.07.523

Classification of Sentimental Reviews Using Machine Learning Techniques

Abinash Tripathy^*, Ankit Agrawal, Santanu Kumar Rath

^*Corresponding author for this work

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

150 Scopus citations

Abstract

Sentiment Analysis is the most prominent branch of natural language processing. It deals with the text classification in order to determine the intention of the author of the text. The intention can be of admiration (positive) or criticism (Negative) type. This paper presents a comparison of results obtained by applying Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm. These algorithms are used to classify a sentimental review having either a positive review or negative review. The dataset considered for training and testing of model in this work is labeled based on polarity movie dataset and a comparison with results available in existing literature has been made for critical examination.

Original language	English (US)
Pages (from-to)	821-829
Number of pages	9
Journal	Procedia Computer Science
Volume	57
DOIs	https://doi.org/10.1016/j.procs.2015.07.523
State	Published - 2015
Event	3rd International Conference on Recent Trends in Computing, ICRTC 2015 - Delhi, India Duration: Mar 12 2015 → Mar 13 2015

Keywords

Classification
Naive Bayes (NB)
Polarity Movie Dataset
Sentiment Analysis
Support Vector Machine (SVM)

ASJC Scopus subject areas

General Computer Science

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10.1016/j.procs.2015.07.523

Cite this

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title = "Classification of Sentimental Reviews Using Machine Learning Techniques",

abstract = "Sentiment Analysis is the most prominent branch of natural language processing. It deals with the text classification in order to determine the intention of the author of the text. The intention can be of admiration (positive) or criticism (Negative) type. This paper presents a comparison of results obtained by applying Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm. These algorithms are used to classify a sentimental review having either a positive review or negative review. The dataset considered for training and testing of model in this work is labeled based on polarity movie dataset and a comparison with results available in existing literature has been made for critical examination.",

keywords = "Classification, Naive Bayes (NB), Polarity Movie Dataset, Sentiment Analysis, Support Vector Machine (SVM)",

author = "Abinash Tripathy and Ankit Agrawal and Rath, {Santanu Kumar}",

note = "Publisher Copyright: {\textcopyright} 2015 The Authors. Published by Elsevier B.V.; 3rd International Conference on Recent Trends in Computing, ICRTC 2015 ; Conference date: 12-03-2015 Through 13-03-2015",

year = "2015",

doi = "10.1016/j.procs.2015.07.523",

language = "English (US)",

volume = "57",

pages = "821--829",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier B.V.",

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TY - JOUR

T1 - Classification of Sentimental Reviews Using Machine Learning Techniques

AU - Tripathy, Abinash

AU - Agrawal, Ankit

AU - Rath, Santanu Kumar

PY - 2015

Y1 - 2015

N2 - Sentiment Analysis is the most prominent branch of natural language processing. It deals with the text classification in order to determine the intention of the author of the text. The intention can be of admiration (positive) or criticism (Negative) type. This paper presents a comparison of results obtained by applying Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm. These algorithms are used to classify a sentimental review having either a positive review or negative review. The dataset considered for training and testing of model in this work is labeled based on polarity movie dataset and a comparison with results available in existing literature has been made for critical examination.

AB - Sentiment Analysis is the most prominent branch of natural language processing. It deals with the text classification in order to determine the intention of the author of the text. The intention can be of admiration (positive) or criticism (Negative) type. This paper presents a comparison of results obtained by applying Naive Bayes (NB) and Support Vector Machine (SVM) classification algorithm. These algorithms are used to classify a sentimental review having either a positive review or negative review. The dataset considered for training and testing of model in this work is labeled based on polarity movie dataset and a comparison with results available in existing literature has been made for critical examination.

KW - Classification

KW - Naive Bayes (NB)

KW - Polarity Movie Dataset

KW - Sentiment Analysis

KW - Support Vector Machine (SVM)

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U2 - 10.1016/j.procs.2015.07.523

DO - 10.1016/j.procs.2015.07.523

M3 - Conference article

AN - SCOPUS:84944037636

SN - 1877-0509

VL - 57

SP - 821

EP - 829

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 3rd International Conference on Recent Trends in Computing, ICRTC 2015

Y2 - 12 March 2015 through 13 March 2015

ER -

Classification of Sentimental Reviews Using Machine Learning Techniques

Abstract

Keywords

ASJC Scopus subject areas

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