Toward Prompt-Enhanced Sentiment Analysis with Mutual Describable Information Between Aspects

Figures & data

Figure 1. An illustration.

Figure 2. The diagram illustrating the model structure.

Table 1. Partitions of datasets.

Dataset	Positive		Neutral		Negative
	Training	Test	Training	Test	Training	Test
Restaurant	2164	728	637	196	196	196
Laptop	994	341	464	169	169	128
Twitter	1561	173	3127	346	346	173

Table 2. Outcomes (the outcomes of PESA are written in bold.

	Restaurant		Laptop		Twitter
Method	Accuracy	Macro-F1	Accuracy	Macro-F1	Accuracy	Macro-F1
SVM*	80.16	-	70.49	-	63.40	63.30
IAN*	78.60	-	72.10	-	-	-
TD-LSTM*	75.63	-	68.13	-	70.80	69.00
MemNet*	78.16	65.83	70.33	64.09	68.50	66.91
TD-GAT-BERT*	83.00	-	79.80	-	-	-
CDT*	81.78	72.32	75.73	71.42	74.00	72.19
BERT-SPC	84.46	76.98	78.99	75.03	73.55	72.14
AEN-BERT	83.12	73.76	79.93	76.31	74.71	73.13
PESA (Ours)	85.36	77.93	79.58	76.82	74.58	73.57
Ablation
PESA	85.36	77.93	79.58	76.82	74.58	73.57
PESA w/o MIM	84.72	77.48	79.34	76.57	73.97	73.26
PESA w/o SP	84.53	77.02	79.16	75.68	73.61	72.54

Models designated using “*” indicate that they were quoted from the original paper. Competitive outcomes on all three datasets are presented. The proposed method outperforms all baseline models on the Restaurant dataset, with Accuracy improving by 0.1%. However, it has generated slightly less accuracy in the Twitter and laptop data sets. Alternatively, the suggested algorithm outperforms all once Macro-F1 is a concern. When compared with the three baseline models based on BERT, the proposed model has better results, indicating that using various information, such as the information of the phrase structure and grammatical information in the BERT’s intermediate layer, can help solve the aspect-based sentiment analysis problem better. The experiment suggests that the semantic information mastered by the higher layers of BERT is more significant than others in the task because the BERT-SPC model, without using intermediate layers as control information, succeeds in quite good outcomes.

Table 3. A case study.

Sentence	Label	Ours
Although the restaurant [MASK] is nice, I prefer not to go for the food [MASK].	negative	negative
I love the drinks, esp the lychee martini, and the food [MASK] is also VERY good.	positive	positive
I had to wait for my friend at the bar [MASK] for a few minutes	neutral	neutral
Great food [MASK] but the service [MASK] was dreadful!	negative	negative
The staff members are extremely friendly and even replaced my drink [MASK] once when I dropped it outside.	neutral	neutral
Average to good Thai food [MASK], but terrible [MAKS] delivery	positive	positive
The waitstaff is solicitous and friendly and always seems glad to see us, and the food [MASK] is wonderful, if not stunningly creative.	positive	positive

Figure 3. Attention scores’ heat map.