Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
Xuechun Li, Xueyao Sun, Zewei Xu, Yifan Zhou
Abstract
In this paper, we conduct a sentence-level sentiment analysis on the product reviews from Amazon and thorough analysis on the model’s interpretability. For the sentiment analysis task, we mainly use the Bi-LSTM 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 96%. And we find that the aspect terms have the same or even more attention weights than the sentimental words in sentences.
Topics & Concepts
Sentiment analysisAmazon rainforestComputer scienceNatural language processingSentenceArtificial intelligenceProduct (mathematics)Information retrievalData scienceMathematicsBiologyGeometryEcologySentiment Analysis and Opinion MiningTopic ModelingAdvanced Text Analysis Techniques