TY - GEN
T1 - Inferring political preference from Twitter tweets
AU - Shetty, Nisha P.
AU - Teja, Daita Ravi
AU - Vinil, Tummala Srinag
AU - Kanwal, Swati
AU - Mutha, Harsh
AU - Bhargava, Akshita
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/1/20
Y1 - 2021/1/20
N2 - Commercial popularity of social media and free availability of vast data has enhanced the interests of the researchers in analyzing its contents. Many businesses make use of such data to harness the mindset and likes of their target audience, thereby improving their profits. Sentiment analysis of Twitter texts have proved to be an effective way of voicing the needs of large masses and is used by many prominent politicians in making better campaigning strategies. Multiple machine learning classifiers are implemented in this study to access the stance of US citizens towards Democrats/Republicans to deduce which political party a user prefers from his tweets. Performance of a stacked ensemble is compared against a deep neural network for the mentioned problem domain.
AB - Commercial popularity of social media and free availability of vast data has enhanced the interests of the researchers in analyzing its contents. Many businesses make use of such data to harness the mindset and likes of their target audience, thereby improving their profits. Sentiment analysis of Twitter texts have proved to be an effective way of voicing the needs of large masses and is used by many prominent politicians in making better campaigning strategies. Multiple machine learning classifiers are implemented in this study to access the stance of US citizens towards Democrats/Republicans to deduce which political party a user prefers from his tweets. Performance of a stacked ensemble is compared against a deep neural network for the mentioned problem domain.
UR - https://www.scopus.com/pages/publications/85102584468
UR - https://www.scopus.com/pages/publications/85102584468#tab=citedBy
U2 - 10.1109/ICICT50816.2021.9358593
DO - 10.1109/ICICT50816.2021.9358593
M3 - Conference contribution
AN - SCOPUS:85102584468
T3 - Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021
SP - 471
EP - 475
BT - Proceedings of the 6th International Conference on Inventive Computation Technologies, ICICT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th International Conference on Inventive Computation Technologies, ICICT 2021
Y2 - 20 January 2021 through 22 January 2021
ER -