Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
This provides interpretable sentiment analysis for product reviews, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled sentiment analysis on Amazon product reviews using a BiLSTM with attention, achieving up to 0.96 accuracy, and found that aspect terms often receive equal or greater attention than sentimental words.
In this paper, we conduct a sentence level sentiment analysis on the product reviews from Amazon and thorough analysis on the model interpretability. For the sentiment analysis task, we use the BiLSTM model with attention mechanism. For the study of interpretability, we consider the attention weights distribution of single sentence and the attention weights of main aspect terms. The model has an accuracy of up to 0.96. And we find that the aspect terms have the same or even more attention weights than the sentimental words in sentences.