TY - GEN
T1 - Sentiment analysis for the Greek language
AU - Spatiotis, Nikolaos
AU - Mporas, Iosif
AU - Paraskevas, Michael
AU - Perikos, Isidoros
PY - 2016/11/10
Y1 - 2016/11/10
N2 - In recent years, the rapid development of technology has invaded dynamically in all areas of human activity by facilitating communication, information and interaction between people worldwide through Internet useternet users are no longer passive recipients of information, but new information data creators expressing ideas, opinions, feelings or their views on a service-product. Taking into account the evolution and use of mobile devices and the proliferation of wireless networks, the timely and widespread use of social networks and services satisfying the above uses are understandable this paper, we present an approach to analyze textual data in Greek language and extract meaningful information regarding the writer's opinion. More specifically, we present a supervised approach which classifies user generated comments into the proper polarity category. An extensive experimental study was conducted in the context of users' attitudes and opinions on e-lectures that they attended. The results were very promising, indicating that the approach was accurate and able to correctly classify opinions into the proper category.
AB - In recent years, the rapid development of technology has invaded dynamically in all areas of human activity by facilitating communication, information and interaction between people worldwide through Internet useternet users are no longer passive recipients of information, but new information data creators expressing ideas, opinions, feelings or their views on a service-product. Taking into account the evolution and use of mobile devices and the proliferation of wireless networks, the timely and widespread use of social networks and services satisfying the above uses are understandable this paper, we present an approach to analyze textual data in Greek language and extract meaningful information regarding the writer's opinion. More specifically, we present a supervised approach which classifies user generated comments into the proper polarity category. An extensive experimental study was conducted in the context of users' attitudes and opinions on e-lectures that they attended. The results were very promising, indicating that the approach was accurate and able to correctly classify opinions into the proper category.
KW - Feature vectors
KW - Machine learning
KW - Sentiment analysis
KW - Sentiment classification
KW - Text Mining
UR - http://www.scopus.com/inward/record.url?scp=85014864683&partnerID=8YFLogxK
U2 - 10.1145/3003733.3003769
DO - 10.1145/3003733.3003769
M3 - Conference contribution
AN - SCOPUS:85014864683
T3 - ACM International Conference Proceeding Series
BT - PCI 2016 - 20th Pan-Hellenic Conference on Informatics
PB - ACM Press
T2 - 20th Pan-Hellenic Conference on Informatics, PCI 2016
Y2 - 10 November 2016 through 12 November 2016
ER -