Building a Model for finding Quality of Affirmation in a Discussion Forum
| Autor: | Srinath Srinivasa, Sridhar Mandyam, Prakhar Mishra, Jagatdeep Pattanaik, Aparna Lalingkar |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Mediation (Marxist theory and media studies) media_common.quotation_subject Aggregate (data warehouse) 02 engineering and technology Epistemology Order (exchange) 020204 information systems Mediation 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Quality (business) Classifier (UML) Sentence media_common |
| Zdroj: | ICALT |
| DOI: | 10.1109/icalt49669.2020.00043 |
| Popis: | Education is an inherently social activity. People like to exchange thoughts and learn from each other: this is why we are interested in discussions. But discussions can be messy and vague. In order to make discussions meaningful, relevant mediations may need to be made, whenever discussions lose clarity. Discussion forum data is in the form of a sequence of questions, answers, and comments on the answers. Taken together, this data is called an affirmation. Knowing the clarity of an affirmation makes it possible to intervene in a discussion to steer it towards agreement or conclusion. We have built a model of clarity for a branch of an affirmation which is considered based on scores of agreements, disagreements, and partial answers. We used three classifier models in our experimentation, but the random forest classifier model gave an F1 score of 0.7315, which was better than the other two classifier models. For our model, the ratio of strong agreement to partial agreement was 1.44 for training data and 1.37 for testing data, which is quite close. In the future, we are planning to enhance our model at the sentence level to capture more details and find an aggregate score of clarity for an affirmation. |
| Databáze: | OpenAIRE |
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