TY - GEN
T1 - Analyzing Stance and Topic of E-Cigarette Conversations on Twitter
T2 - 11th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2021
AU - Sari Kaunang, Cristin Purnama
AU - Amastini, Fitria
AU - Mahendra, Rahmad
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/27
Y1 - 2021/1/27
N2 - To control the use of e-cigarette, Indonesia plan to establish a regulation that embodies all the concerns, sentiments, and opinions of public This study aims to identify public opinions in social media Twitter by classifying tweets into group in favor or against e-cigarette and explore dominant topics of each group. This research obtained 15,373 tweets between June 2019 - May 2020 that is classified into 4 labels: Against, Favor, Neutral, and Irrelevant. The best model was selected with specification: 3 features (Count, Unigram, and Bigram), Logistic Regression algorithm, and three-stage classification pipeline (\mathrm{F}1-\text{score}=0.807). As for topic modelling, corpus Against and Favor are used to retrieve dominant topics. We chose Non-negative Matrix Factorization algorithm with \mathrm{k}=6 and achieve high coherence scores, which are 0.962004 for corpus Against and 0.999736 for corpus Favor.
AB - To control the use of e-cigarette, Indonesia plan to establish a regulation that embodies all the concerns, sentiments, and opinions of public This study aims to identify public opinions in social media Twitter by classifying tweets into group in favor or against e-cigarette and explore dominant topics of each group. This research obtained 15,373 tweets between June 2019 - May 2020 that is classified into 4 labels: Against, Favor, Neutral, and Irrelevant. The best model was selected with specification: 3 features (Count, Unigram, and Bigram), Logistic Regression algorithm, and three-stage classification pipeline (\mathrm{F}1-\text{score}=0.807). As for topic modelling, corpus Against and Favor are used to retrieve dominant topics. We chose Non-negative Matrix Factorization algorithm with \mathrm{k}=6 and achieve high coherence scores, which are 0.962004 for corpus Against and 0.999736 for corpus Favor.
KW - classification method
KW - e-cigarettes
KW - stance detection
KW - text mining
KW - topic modelling
KW - vape
UR - http://www.scopus.com/inward/record.url?scp=85103466958&partnerID=8YFLogxK
U2 - 10.1109/CCWC51732.2021.9375949
DO - 10.1109/CCWC51732.2021.9375949
M3 - Conference contribution
AN - SCOPUS:85103466958
T3 - 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021
SP - 304
EP - 310
BT - 2021 IEEE 11th Annual Computing and Communication Workshop and Conference, CCWC 2021
A2 - Paul, Rajashree
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 27 January 2021 through 30 January 2021
ER -