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Item-Based Collaborative Filtering Using Sentiment Analysis of User Reviews

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Applications of Computing and Communication Technologies (ICACCT 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 899))

Abstract

Traditional Collaborative filtering algorithm works by using only the past experience of a user. To overcome the limitations of the traditional collaborative algorithm, an item based collaborative filtering system was introduced. In this paper, an improved recommender system is proposed. A dictionary of sentiment scores is created. These sentiment scores are calculated by finding the probability of the reviews to be positive. This sentiment score is used by an item based collaborative filtering system to improve the recommendations and filter out items with overall negative user opinion. The performance of the proposed system is compared with previous work done in this field.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Inderprastha Engineering College, Ghaziabad, India

    Abhishek Dubey, Ayush Gupta, Nitish Raturi & Pranshu Saxena

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  1. Abhishek Dubey
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  2. Ayush Gupta
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  3. Nitish Raturi
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  4. Pranshu Saxena
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Corresponding author

Correspondence to Abhishek Dubey .

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Editors and Affiliations

  1. Ministry of Skill Development, Delhi, India

    Ganesh Chandra Deka

  2. School of Science and Technology, Nottingham Trent University, Nottingham, UK

    Omprakash Kaiwartya

  3. Department of Computer Science, Shyama Prasad Mukherji College, University of Delhi, Delhi, India

    Pooja Vashisth

  4. Department of Computer Science, North Campus, University of Delhi, Delhi, India

    Priyanka Rathee

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Dubey, A., Gupta, A., Raturi, N., Saxena, P. (2018). Item-Based Collaborative Filtering Using Sentiment Analysis of User Reviews. In: Deka, G., Kaiwartya, O., Vashisth, P., Rathee, P. (eds) Applications of Computing and Communication Technologies. ICACCT 2018. Communications in Computer and Information Science, vol 899. Springer, Singapore. https://doi.org/10.1007/978-981-13-2035-4_8

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  • DOI: https://doi.org/10.1007/978-981-13-2035-4_8

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  • Print ISBN: 978-981-13-2034-7

  • Online ISBN: 978-981-13-2035-4

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