Aspect-based Sentiment Analysis of Scientific Reviews - Openreview dataset

<p>The dataset contains all the data used in the JCDL 2020 research paper: <a href="https://dl.acm.org/doi/10.1145/3383583.3398541">Aspect-based Sentiment Analysis of Scientific Reviews</a></p> <p>The dataset is split into multiple files containing&nbsp;all the sentence annotations and the ICLR open review dataset (with reviews and scores and the confidence scores, final recommendation, etc.) for the last three years.</p> <p>The file &quot;iclr_conf.p&quot; is a pickle file which contains a NumPy array object.<br> The array contains 2681 rows corresponding to each accepted or rejected paper of 2017,2018,2019<br> Each row contains 4 columns.<br> The first column is the link of the paper in openreview.net, from where the data related to the paper is collected.<br> The second column is either 0 or 1, corresponding to the final decision: rejection or acceptance respectively.<br> The third column is the year of the conference for the particular submission.<br> The fourth column is another NumPy array containing 3 reviews in 3 rows. Each row of this array contains 3 columns containing the list of sentences in the same sequence as it appears in the text of the review, the confidence(ranging from 1-5), and the rating(ranging(1-10)) respectively.</p> <p>Each line of the file &quot;sentences.csv&quot; contains one sentence whose corresponding annotation is provided in the corresponding line in the file &quot;annotations.csv&quot;<br> The file &quot;annotations.csv&quot; is a file containing 8 comma-separated integers in each line.<br> Each column corresponds to the following aspects: Appropriateness, Clarity, Originality, Empirical/Theoretical Soundness, Meaningful Comparison, Substance,<br> Impact of Dataset/Software/Ideas and Recommendation.<br> An integer 0,1,2,3 corresponds to the following sentiment labels of the sentence on that aspect: Absent, Positive, Negative, Neutral</p> <p>Please cite our paper published in JCDL-2020 if you use our data: <a href="https://dl.acm.org/doi/10.1145/3383583.3398541">https://dl.acm.org/doi/10.1145/3383583.3398541</a></p>