Given the task definition and input, reply with output. In this task, you are given a paper review. Based on the review, your job is to identify language and generate "en" if the review is in English or generate "es" if the review is in Spanish. Note that URLs in the text have been replaced with [Link].

- Strengths: This paper tackles an interesting problem and provides a (to my knowledge) novel and reasonable way of learning and combining cognitive features with textual features for sentiment analysis and irony detection. The paper is clearly written and organized, and the authors provided a lot of useful detail and informative example and plots. Most of the results are convincing, and the authors did a good job comparing their approach and results with previous work. - Weaknesses: 1. Just from the reading abstract, I expected that the authors' approach would significantly outperform previous methods, and that using both the eye-gaze and textual features consistently yields the best results. Upon reading the actual results section, however, it seems like the findings were more mixed. I think it would be helpful to update the abstract and introduction to reflect this. 2. When evaluating the model on dataset 1 for sentiment analysis, were the sarcastic utterances included? Did the model do better on classifying the non-sarcastic utterances than the sarcastic ones? 3. I understand why the eye-movement data would be useful for sarcasm detection, but it wasn't as obvious to me why it would be helpful for (non-sarcastic) sentiment classification beyond the textual features. - General Discussion: This paper contains a lot of interesting content, and the approach seems solid and novel to me. The results were a little weaker than I had anticipated from the abstract, but I believe would still be interesting to the larger community and merits publication.
en