Abstract:
Growth in online gaming, the number of reviews for every game has grown exponentially.
Because of this, it has actually become tough for developers and analysts to find out the
accurate sentiment of the users. Sentiment analysis is normally applied to textual data;
however, state-of-the-art models have failed in understanding subtle cues in text-sarcasm and
context in gaming reviews. It detects these lacunas and thus proposes a hybrid model for the
detection of sentiment and sarcasm, leveraging BERT with an added layer for capturing subtle
language cues related to sarcasm. Based on a dataset of gaming reviews from the Steam gaming
platform, this model leverages high-level NLP software techniques for transparency, enhancing
interpretability via Explainable AI. The experimental results prove the effectiveness of the
proposed approach to yield high accuracy and robust performance in detecting both sentiments
and sarcasms, thus giving valuable insights to developers and stakeholders.
