Treffer: Sentiment Analysis of Aníkúlápó Movie Reviews on Twitter Using Big Data Analytics.

Big Data is an all-encompassing term that refers to the accumulation of data in large pools employed in today’s global corporate world. Twitter is one such Big Data platform with over millions of tweets daily. Reading through the humongous volume of tweets posted with reference to an event such as a movie is practically impossible. Aníkúlápó is a movie of Nigerian origin that became the most viewed non-English film within a few weeks of its release on Netflix and in cinemas. The movie has been trending on social media, especially Twitter. While some reviews considered the movie as a literary masterpiece with several lessons to be learnt from it, some are either critical or indifferent. The paper explores the efficacy of analyzing the sentiments expressed on Twitter about the movie “Aníkúlápó” using Big Data Analytics. Data was programmatically obtained from Twitter using Twitter Application Programming Interface (API). Purposive sampling technique was adopted for sampling the reviews, and the major criterion for selecting a review was the inclusion or presence of the word “Anikulapo” in an original tweet, not retweet. Python programming language, Tweepy, TextBlob and Pandas were used for downloading, wrangling, cleaning, and analyzing the data. The findings showed that most tweets expressed positive sentiments, and the analysis of engagement metrics showed that tweets with positive sentiments received more likes. [ABSTRACT FROM AUTHOR]

Copyright of IUP Journal of Computer Sciences is the property of IUP Publications and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)