Publication Date: 2023/06/20
Abstract: User-generated content, such as reviews and comments, contains valuable information about products as well as the opinions expressed by users. With the rise of internet usage, there has been an influx of usergenerated data in the form of reviews and comments. Individuals share their experiences, opinions, sentiments, and emotions by writing reviews and comments about products they have purchased online or their impressions after engaging with various media, such as movies or books. These user-generated data often encompass emotions such as happiness, sadness, and surprise, which can serve as crucial indicators for recommending new items based on users' emotional preferences. In this study, we propose a method to extract emotions from user-generated data by utilizing lexical ontology, specifically WordNet, in conjunction with insights from the field of psychology. These extracted emotions can then be leveraged to enhance recommendations. To evaluate the effectiveness of our approach, we compare our emotion prediction model with the traditional rating-based item similarity model, as well as explore the impact of emotional fuzziness in the feature space.
Keywords: No Keywords Available
DOI: https://doi.org/10.5281/zenodo.8059970
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23JUN240.pdf
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