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Uniform Textual Feedback Analysis for Effective Sentiment Analysis

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Knowledge Graphs and Semantic Web (KGSWC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1459))

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Abstract

In this paper, a machine learning-based methodology is proposed to measure the users’ sentiments expressed in textual feedback. The methodology uses collaborative filtering to evaluate the degree of positivity or negativity for every important aspect from each user’s perspective participating in the feedback analysis, hence proposing a uniform feedback analysis. Key aspects of a particular item or an issue are identified through topic modeling and taking into account the syntactic and semantic properties of words after processing the merged document obtained from all the feedbacks. Aggregate sentiment of an item is evaluated by considering the importance and sentiments of key aspects. This methodology can be used to analyze textual feedbacks of any domain with very little domain-dependent information. In this paper, feedbacks of two different domains have been analyzed and presented. Results show that the performances of the same items of different brands can be compared easily.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University Institute of Engineering and Technology, CSJM University, Kanpur, Kanpur, India

    Alok Kumar & Renu Jain

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  1. Alok Kumar
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  2. Renu Jain
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Editor information

Editors and Affiliations

  1. Tinamica/Universidad Internacional de la Rioja, Madrid, Madrid, Spain

    Boris Villazón-Terrazas

  2. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Fernando Ortiz-Rodríguez

  3. Tamaulipas Autonomous University, Ciudad Victoria, Mexico

    Sanju Tiwari

  4. Texas A&M University – Kingsville, Kingsville, TX, USA

    Ayush Goyal

  5. Vardhaman College of Engineering, Hyderabad, Telangana, India

    MA Jabbar

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Kumar, A., Jain, R. (2021). Uniform Textual Feedback Analysis for Effective Sentiment Analysis. In: Villazón-Terrazas, B., Ortiz-Rodríguez, F., Tiwari, S., Goyal, A., Jabbar, M. (eds) Knowledge Graphs and Semantic Web. KGSWC 2021. Communications in Computer and Information Science, vol 1459. Springer, Cham. https://doi.org/10.1007/978-3-030-91305-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-91305-2_21

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-91305-2

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