Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 43 references that are in the map. First posted on arXiv 11 Nov 2021.
Builds on
8 of 43 references.
References · 8
- 2018Deep contextualized word representationscited by 2,290
- 2014Convolutional Neural Networks for Sentence Classificationcited by 1,127
- 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 546
- 2019Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentencecited by 122
- 2013From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviewscited by 108
- 2018Aspect Term Extraction with History Attention and Selective Transformationcited by 37
- 2020Sentiment Analysis: Automatically Detecting Valence, Emotions, and Other Affectual States from Textcited by 26
- 2016A Joint Model for Word Embedding and Word Morphologycited by 14
Built on by
Not cited yet.