TY - GEN
T1 - Improved sentiment urgency emotion detection for business intelligence
AU - Soussan, Tariq
AU - Trovati, Marcello
PY - 2021
Y1 - 2021
N2 - The impact of social media on people’s lives has significantly grown over the last decade. Individuals use it to promote discussions and a way of acquiring data. Industries use social media to market their goods and facilities, advise and inform clients about future offers, and follow up on their direct market. It also offers vital information concerning the general emotions and sentiments directly connected to welfare and security. In this work, an improved model called Improved Sentiment Urgency Emotion Detection (ISUED) has been created based on previous work for opinion and social media mining implemented with Multinomial Naive Bayes algorithm and based on three classifiers which are sentiment analysis, urgency detection, and emotion classification. The model will be trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is elevated. This model will be applied on the same data set of previous work acquired from a general business Twitter account of one of the largest chains of supermarkets in the United Kingdom to be able to see what sentiments and emotions can be detected and how urgent they are.
AB - The impact of social media on people’s lives has significantly grown over the last decade. Individuals use it to promote discussions and a way of acquiring data. Industries use social media to market their goods and facilities, advise and inform clients about future offers, and follow up on their direct market. It also offers vital information concerning the general emotions and sentiments directly connected to welfare and security. In this work, an improved model called Improved Sentiment Urgency Emotion Detection (ISUED) has been created based on previous work for opinion and social media mining implemented with Multinomial Naive Bayes algorithm and based on three classifiers which are sentiment analysis, urgency detection, and emotion classification. The model will be trained to improve its accuracy and F1 score so that the precision and percentage of correctly predicted texts is elevated. This model will be applied on the same data set of previous work acquired from a general business Twitter account of one of the largest chains of supermarkets in the United Kingdom to be able to see what sentiments and emotions can be detected and how urgent they are.
UR - http://www.scopus.com/inward/record.url?scp=85090100363&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090100363&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-57796-4_30
DO - 10.1007/978-3-030-57796-4_30
M3 - Conference proceeding (ISBN)
AN - SCOPUS:85090100363
SN - 9783030577957
T3 - Advances in Intelligent Systems and Computing
SP - 312
EP - 318
BT - Advances in Intelligent Networking and Collaborative Systems - The 12th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2020
A2 - Barolli, Leonard
A2 - Li, Kin Fun
A2 - Miwa, Hiroyoshi
PB - Springer
T2 - 12th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2020
Y2 - 31 August 2020 through 2 September 2020
ER -