Opinion Units: Concise and Contextualized Representations for Aspect-Based Sentiment Analysis
Authors: Emil Häglund, Johanna Björklund


Reviewer paper summaries: In this paper, the authors approach ABSA through the creation of "opinion units" for prompting with generative LLMs. These opinion units function as structured summaries of the review that contain the sentiment, aspect and opinion. Concretely, the authors investigate the performance of GPT 3.5 in a few-shot setting. The proposed methodology is evaluated on the Yelp review dataset as well as the Res15 and Res16 datasets from a SemEval shared task. The evaluation includes two main parts: (1) opinion units, which is excerpt-based and (2) opinion retrieval to compare the performance of different chunking strategies.
Summary of strengths: 1. The paper is quite well-written, clear and easy to follow.

2. The authors employ an elaborate framework based on propositions (atomicity, injectivity, completeness and contextuality) tailored to the ABSA domain.

3. The paper contains clear and appropriate visualizations and examples to accompany the text.

4. The applied methodology is explained in sufficient detail and many of the experimental choices are well argued for.

5. The authors present an analysis of system errors using a detailed error taxonomy.
Summary of weaknesses: 1. The applied methodology is only evaluated for GPT 3.5, which is rapidly becoming outdated. As this is the only evaluated model and it is proprietary, it is hard to generalize the results. The soundness could be increased significantly by including open-source models.

2. The proposed methodology is lacking comparison to baselines. Although the authors use common benchmark dataset, the paper does not contain direct comparisons to any existing systems. As the authors also do not provide F1-scores, Accuracy and Recall, it is hard to estimate the actual performance of the model. This is especially problematic because there is only one model being evaluated.
Comments, suggestions and typos: 1. The paper is missing a reference for the Res15 and Res16 SemEval datasets.

2. It is not clearly explained what is meant by @5 @10 and @20 Precision.

3. How did the authors determine the temperature value of 1.3? They mention it was found effective, but why this value in particular?

4. There are still some typo's  or linguistic mistakes in the paper. Here are some examples (not exhaustive):

197: "Since method introduced here encourages ..." 
266-267: " However, sentences can include multiple aspect ..." 
306-307: "the aspect label generated by the LLM facilitate ..."
Overall assessment scores: 2