Hasn: A hierarchical attention-guided network for robust sentiment analysis of movie reviews

Abstract

Sentimental analysis (SA) of movie reviews has become an essential means of supporting audiences, filmmakers, and investors to facilitate data-driven marketing, support audience engagement, and maximize audience development returns. Nevertheless, current SA models are yet to overcome potentially daunting problems such as noisy data, ambiguity of language, limited handling of negations, and a loss of rich semantic information during mirror preprocessing that ultimately decreases classification performance. To overcome this hurdle, a unique two-stage strategy entitled Hierarchical Attention-Guided Sentiment Network (HASN) is proposed that enables reliable and interpretable sentiment predictions. The first stage includes a clearly defined preprocessing pipeline of the IMDb dataset that features text cleaning, lemmatizing, removing stop words, negation, TF-IDF filtering of low-frequency terms, and lastly, Word2Vec embeddings, which create semantically consistent and contextually rich representations of potential inputs. The second stage integrates a tri-stream attention recurrent extractor (TARE), which reconstructs and merges previously learned outputs with the learned syntactic and semantic contexts with the benefits of stacked Bi-LSTMs, combines learned syntactic and semantic contexts with multi-head attention to identify the tokens that drive sentiment, and ends with a residual-normalized GRU (ResNorm-GRU), which sequentially refines the sequential process of intake. The attentive conditional random field (Attn-CRF) serves as a classifier by boosting coherence in label predictions through attention weighting with modeling of the sequential dependencies. The experiments with the IMDb dataset demonstrated that the best results detected were the following: 98.09, 98.47, 97.70, and 98.08%. In summary, the HASN framework is a precise, scalable, and interpretable model for analyzing sentiments in movie reviews.