TY - JOUR
T1 - Human context in Sentiment Analysis symbolic technique
AU - Amo-Filvà, Daniel
AU - Usart, Mireia
AU - Grimalt-Álvaro, Carme
AU - Chen, Jiahui
N1 - Funding Information:
We appreciate the support received by Silvia Blasi, Aleix Ollé, and Ángel García in the manual labelling process. This project has been funded by the Social Observatory of the “la Caixa” Foundation as part of the project LCF/PR/SR19/52540001.
Publisher Copyright:
© 2022 Copyright for this paper by its authors.
PY - 2022
Y1 - 2022
N2 - Learning methodologies in Virtual Learning Environments that encourage students' written communication require additional effort from the trainers, in terms of management and sentimental awareness of both, the group and each participant. Analysing and evaluating sentiment for every message in every conversation is a hard and tedious work. This is one of the reasons why Natural Language Processing (NLP) and Sentiment Analysis (SA) are gaining popularity. The idea of automating the processes of emotional evaluation of students' conversations in an academic context invites us to consider those automatisms as substitutes for manual processes, such as SA. The challenge of including the human context, together with treating the data with adequate privacy in terms of current legislation, makes these techniques complex. There are two main techniques in SA, those based on lexicons and those based on machine learning. In the present study, results of SA based on two different lexicons, are compared with the results of a manual labelling performed by human trainers to test the effectiveness of the SA technique. Regarding the privacy concerns, an open-source local analysis tool was updated and incorporated such automated processes, both for the present study and for trainers to use considering the extracted results. The results show that lexical-based SA processes tend to consider messages towards the extremes (positive/negative), while human beings' evaluation tends towards sentimental neutrality, both in female and male.
AB - Learning methodologies in Virtual Learning Environments that encourage students' written communication require additional effort from the trainers, in terms of management and sentimental awareness of both, the group and each participant. Analysing and evaluating sentiment for every message in every conversation is a hard and tedious work. This is one of the reasons why Natural Language Processing (NLP) and Sentiment Analysis (SA) are gaining popularity. The idea of automating the processes of emotional evaluation of students' conversations in an academic context invites us to consider those automatisms as substitutes for manual processes, such as SA. The challenge of including the human context, together with treating the data with adequate privacy in terms of current legislation, makes these techniques complex. There are two main techniques in SA, those based on lexicons and those based on machine learning. In the present study, results of SA based on two different lexicons, are compared with the results of a manual labelling performed by human trainers to test the effectiveness of the SA technique. Regarding the privacy concerns, an open-source local analysis tool was updated and incorporated such automated processes, both for the present study and for trainers to use considering the extracted results. The results show that lexical-based SA processes tend to consider messages towards the extremes (positive/negative), while human beings' evaluation tends towards sentimental neutrality, both in female and male.
KW - Human Context
KW - Natural Language Processing
KW - Sentiment Analysis
KW - Word List
UR - http://www.scopus.com/inward/record.url?scp=85139879963&partnerID=8YFLogxK
M3 - Conference article
AN - SCOPUS:85139879963
SN - 1613-0073
VL - 3238
SP - 61
EP - 69
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
T2 - 10th Learning Analytics Summer Institute Spain, LASI Spain 2022
Y2 - 20 June 2022 through 21 June 2022
ER -