TY - GEN
T1 - Using machine learning for sentiment and social influence analysis in text
AU - Kolog, Emmanuel Awuni
AU - Montero, Calkin Suero
AU - Toivonen, Tapani
N1 - Publisher Copyright:
© Springer International Publishing AG 2018.
PY - 2018
Y1 - 2018
N2 - Students’ academic achievement is largely driven by their social phenomena, which is shaped by social influence and opinion dynamics. In this paper, we employed a machine learning technique to detect social influence and sentiment in text-based students’ life stories. The life stories were first pre-processed and clustered using k-means with euclidean distance. After that, we identified domestic, peer and school staff as the main influences on students’ academic development. The various influences were used as class labels for supervised classification using SMO, MNB and J48 decision tree classifiers. In addition, the stories were manually labelled with positive and negative sentiments. We employed 10-folds cross-validation in classifying the sentiments and the social influences in the story corpus. The result show that peer influence is more salient on students’ academic development followed by staff (15%) and domestic influences (12%). However, the remaining 54% of the stories contains unrelated social and other influences. Also, Students expressed more negative sentiment towards academic engagement than the positive sentiments. As per the classifier performance, SMO was found to be superior over MNB and J48 in the sentiment classification while MNB also performed slightly better than the SMO and J48 in the social influence analysis.
AB - Students’ academic achievement is largely driven by their social phenomena, which is shaped by social influence and opinion dynamics. In this paper, we employed a machine learning technique to detect social influence and sentiment in text-based students’ life stories. The life stories were first pre-processed and clustered using k-means with euclidean distance. After that, we identified domestic, peer and school staff as the main influences on students’ academic development. The various influences were used as class labels for supervised classification using SMO, MNB and J48 decision tree classifiers. In addition, the stories were manually labelled with positive and negative sentiments. We employed 10-folds cross-validation in classifying the sentiments and the social influences in the story corpus. The result show that peer influence is more salient on students’ academic development followed by staff (15%) and domestic influences (12%). However, the remaining 54% of the stories contains unrelated social and other influences. Also, Students expressed more negative sentiment towards academic engagement than the positive sentiments. As per the classifier performance, SMO was found to be superior over MNB and J48 in the sentiment classification while MNB also performed slightly better than the SMO and J48 in the social influence analysis.
KW - Clustering
KW - Sentiment
KW - Social influence
KW - Student
KW - Text classification
UR - http://www.scopus.com/inward/record.url?scp=85041039104&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-73450-7_43
DO - 10.1007/978-3-319-73450-7_43
M3 - Conference contribution
AN - SCOPUS:85041039104
SN - 9783319734491
T3 - Advances in Intelligent Systems and Computing
SP - 453
EP - 463
BT - Proceedings of the International Conference on Information Technology and Systems, ICITS 2018
A2 - Rocha, Alvaro
A2 - Guarda, Teresa
PB - Springer Verlag
T2 - International Conference on Information Technology and Systems, ICITS18
Y2 - 11 January 2017 through 13 January 2017
ER -