IDEAS home Printed from https://ideas.repec.org/a/ids/ijdmmm/v15y2023i2p169-183.html
   My bibliography  Save this article

Identification of relevant features influencing movie reviews using sentiment analysis

Author

Listed:
  • Isha Gupta
  • Indranath Chatterjee
  • Neha Gupta

Abstract

Sentiment analysis is a systematic text mining research that examines individuals' behaviour, approach, and viewpoint. This paper analyses viewers' sentiments towards the movies released during the pandemic. This study employs the sentiment analysis techniques on movie reviews' accessed in real-time from internet movie database (IMDb). The paper's main objective is to identify the potential words that contribute to the biases of the reviews and influence overall viewers. The proposed methodology has employed valence aware dictionary for sentiment reasoning based on sentiment analysis of overall reviews, followed by application to various movie genres. Finally, we have applied Pearson's correlation analysis to find the association between the words among the genres. The paper also calculates the sentiment scores of reviews using different sentiment analysis models. Our results showed a minimum of 17% features common genre-wise. It reveals sets of most distinct influential words, which may be vital for understanding the nature of the language used for a particular kind of movie.

Suggested Citation

  • Isha Gupta & Indranath Chatterjee & Neha Gupta, 2023. "Identification of relevant features influencing movie reviews using sentiment analysis," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 15(2), pages 169-183.
    • Handle: RePEc:ids:ijdmmm:v:15:y:2023:i:2:p:169-183
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=131395
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijdmmm:v:15:y:2023:i:2:p:169-183. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=342 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.