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Analyzing emotion based movie recommender system using fuzzy emotion features

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Abstract

User generated contents like reviews and comments contain both the information about a given product and also the opinions asserted by the user. With the surge in internet usage, there is a cascade of user generated data such a reviews and comments. People share their experiences, opinions, sentiments and emotions by writing reviews and comments for products they purchase online or after watching a movie, reading books etc. These user generated data contains emotion lexicons such as happiness, sadness, and surprise. Analysis of such emotion can provide a new aspect for recommending new items based on their emotional preferences. In this work, we extract the emotions from this user generated data using the lexical ontology, WordNet and information from the domain of psychology. These extracted emotions can be used for recommendations. Evaluation on emotion prediction further verifies the effectiveness of the proposed model in comparison to traditional rating based item similarity model. We further compare this with fuzziness in emotion features.

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Notes

  1. MovieLens Dataset: http://grouplens.org/datasets/movielens/.

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Authors and Affiliations

  1. Department of Computer Engineering, NSUT, Delhi, India

    Mala Saraswat, Shampa Chakraverty & Atreya Kala

  2. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, India

    Mala Saraswat

Authors
  1. Mala Saraswat
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  2. Shampa Chakraverty
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  3. Atreya Kala
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Correspondence to Mala Saraswat.

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Saraswat, M., Chakraverty, S. & Kala, A. Analyzing emotion based movie recommender system using fuzzy emotion features. Int. j. inf. tecnol. 12, 467–472 (2020). https://doi.org/10.1007/s41870-020-00431-x

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  • DOI: https://doi.org/10.1007/s41870-020-00431-x

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