Clickbait detection for Thai news headline

Name: Sarawoot Kongyoung

keyword: Clickbait

; Text classification

; Word2vec

; Deep learning

; Convolutional neural network

; Naive bayes

; Support vector machine

; Thai language

Abstract: An automatic feature extraction for classifying the clickbait for Thai news headlines is presented. The first corpus of 132,948 Thai headline news was collected. To transform Thai words into features, Word2Vec is utilized to overcome the ambiguity of the word segmentation. Then, the features are automatically extract using a Convolutional Neural Network (CNN). A number of experiments for CNN have been conducted to find the suitable value of the parameters that achieve the best classification result. We found that using a non-static modelling technique together with 50 dimension of Word2vec feature, {2, 3, 4, 5, 6} window size, and epoch equal to 4 achieves the accuracy of 94.37%. The experimental results also showed that the proposed method achieves the best result as compared to the other classification methods such as Support Vector Machine (SVM) and Naïve Bayes, which achieve 92.05% and 89.70%, respectively.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Natsuda Kaothanthong

Role: advisor

Created: 2017

Modified: 2021-05-13

Issued: 2021-05-13

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2017.342

eng

DegreeName: Master of Engineering

Level: Master's Degree

Descipline: Information and Communication Technology for Embedded Systems

Grantor: Thammasat University

©copyrights Thammasat University

ลำดับที่.	ชื่อแฟ้มข้อมูล	ขนาดแฟ้มข้อมูล	จำนวนเข้าถึง	วัน-เวลาเข้าถึงล่าสุด
1	3260sarawoot.pdf	2.96 MB	4	2022-10-03 10:40:36

ใช้เวลา

0.02865 วินาที