Comparative Study of Different Sarcasm Detection Algorithms Based On Behavioral Approach

被引：11

作者：

Ahuja, Ravinder ^{[1
]}

Bansal, Shantanu ^{[1
]}

Prakash, Shuvam ^{[1
]}

Venkataraman, Karthik ^{[1
]}

Banga, Alisha ^{[2
]}

机构：

[1] Jaypee Inst Informat Technol, Noida 201301, India

[2] Thapar Inst Engn & Technol, Patiala 147004, Punjab, India

来源：

8TH INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING & COMMUNICATIONS (ICACC-2018) | 2018年 / 143卷

关键词：

Accuracy; Classification; Sarcasm; Sentiment; Twitter;

D O I：

10.1016/j.procs.2018.10.412

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Sarcasm changes the dichotomy of an apparently negative or positive utterance into its contrary. While a fair amount of work has been done on automatically detecting emotion in human speech, there has been little research on sarcasm detection. In this paper, we have applied 12 classification algorithms (Gradient Boosting, Gaussian Naive Bayes, Adaboost etc.) on 4 types of datasets (Setl, Sett, Set3, Set4) and varied the split ratio of the datasets to check for the accuracy of every algorithm in different situations. We have applied the behavioral approach to sarcasm detection on twitter dataset. In set 4 were, we found gradient boosting to give the best accuracy in all 3 cases of a split ratio-50:50, 25:75, 10:90 (85.14%, 85.71%, and 85.03%). (C) 2018 The Authors. Published by Elsevier B.V.

引用

页码：411 / 418

页数：8

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