Aspect-Based Sentiment Analysis

Task

ABSA
ASC: Aspect-level Sentiment Analysis
In this repo., Aspect means Aspect Category or Term

Dataset

• SemEval-16 restaurant
• SemEval-16 laptop
• sentihood

Models

bert_intermediate.py

1. Bert_Base: basic BertForSequenceClassification
2. Bert_Base_rpos: only for Term Classification. Relative Position id: [200,4,3,2,1,0,0,1,2,3,4,200,200,200] 200: not sentence words including special tokens. max_seq_length value. 0 means aspect terms in sentence.
3. Bert_Attention: Self Att layer for Bert intermediate hidden states (paper title)

bert_attscores.py <- it is my model which show a little bit better performance.
Additional layer to use high attention score words. Attention scores from mean of all attention heads in final self-attention layer, and use final hidden states of that words.

1. Bert_AttScore: mean of top-k attention score words to classifier.
2. Bert_AttScore_RNN: use bi-GRU for top-k attention score words sequential representation.
3. Bert_AttScore_RNN_add: top-k attention score words and some special tokens to bi-GRU. [SEP], [CLS], [ASP words] and so on.