Identification of Buzzers in Skincare Reviews Using a Lexicon-Based Sentiment Analysis Method
DOI:
https://doi.org/10.30871/jaic.v9i5.11005Keywords:
Buzzers, Product Reviews, Skincare, Sentiment Analysis, Lexicon, Classification, Social MediaAbstract
Along with the rapid development of digital technology, social media has become the main platform for consumers to share experiences about products, including skincare products. However, it is not uncommon for reviews provided by users to not reflect authentic experiences, but rather reviews created by certain parties, or buzzers, to manipulate public perception. The presence of buzzers in skincare reviews is important to consider, as they can affect consumer trust and influence purchasing decisions. This study aims to identify the presence of buzzers in skincare product reviews using a lexicon dictionary-based sentiment analysis. Of the 529 comments analyzed, 75 comments showed negative sentiment and 454 comments showed positive sentiment. The classification results revealed that 85.8% of the comments belonged to the non-buzzer category, while 14.2% were indicated as buzzers. Evaluation of the classification model showed high accuracy, reaching 93%, but performance in detecting buzzers was limited, with a recall metric of only 0.50. This shows that while the model managed to classify non-buzzer comments well, there are still difficulties in identifying buzzer comments, mostly due to data imbalance. This research emphasizes the importance of a proper analytical approach in detecting inauthentic reviews to ensure the information consumers receive remains accurate, transparent, and accountable.
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