In the information-overload era, authenticity is critical but elusive, while fake news, disinformation, and fraudulent reviews are common but not always easily spotted.
Research in the International Journal of Data Mining, Modelling and Management focuses on one particular aspect of this problem how to identify a fake review, specifically a fake movie review, using sentiment analysis techniques to discern meaning from a given review and determine whether it is genuine or not. The work has implications for movie buffs the world over who might then navigate the endless reviews with confidence. The results should also improve the credibility of the movie industry by helping to identify and remove such fraudulent reviews.
Isha Gupta and Neha Gupta of the Faculty of Computer Applications at the Manav Rachna International Institute of Research and Studies in Faridabad, India, and Indranath Chatterjee of the Department of Computer Engineering at Tongmyong University in Busan, South Korea, have analyzed vast amounts of text data to uncover the specific words that contribute to biases in reviews and their influence on overall viewer sentiment. The team used a “valence-aware” dictionary, one that understands the emotional tone or polarity conveyed by particular words or phrases. Valence can be of a positive, negative, or neutral nature.
The researchers were thus able to identify the influential words in a review associated with a specific genre whether the review was of a comedy, horror, action, drama, or thriller. By using a statistical method known as Pearson’s correlation analysis, they could also identify influential features that distinguish each genre. This sheds light on the language used to describe different kinds of movies. Ultimately, the approach gives the team a quantitative assessment of the sentiment conveyed in a given movie review. Around one in five of the characteristic features of the reviews analysed were common across different genres, suggesting that “subtle changes in the feature set showing distinct discrimination among the words used for positive and negative reviews and also for each genre,” the team writes. ” there is a shallow degree of correlation present genre-wise.”
The significance of this research extends beyond understanding viewer sentiments. The study’s findings have important implications in the realm of identifying fake movie reviews. This approach to analyzing the language and sentiment expressed in a movie review, could allow service providers that host reviews to automatically assess the credibility and reliability of a given review and to flag or remove any from their system that is deemed to be fake or not credible in some way. Such a system would not represent censorship of genuine reviews, of course, but ensure that movie fans and industry professionals would have access to authentic information rather than fake reviews, which might otherwise influence movie choice and the consumer experience and at the bottom-line, industry profits, uptake of sequels and franchises, and overall commercial success.
Gupta, I., Chatterjee, I. and Gupta, N. (2023) ‘Identification of relevant features influencing movie reviews using sentiment analysis’, Int. J. Data Mining, Modelling and Management, Vol. 15, No. 2, pp.169–183.
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