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3 results for “Aspect-based Sentiment Analysis”
AWARE: Dataset for Aspect-Based Sentiment Analysis of Apps Reviews
<p> </p> <p><em><strong>The peer-reviewed paper of AWARE dataset is published in ASEW 2021, and can be accessed through: <a href="http://doi.org/10.1109/ASEW52652.2021.00049">http://doi.org/10.1109/ASEW52652.2021.00049</a>. Kindly cite this paper when using AWARE dataset.</strong></em></p> <p> </p> <p>Aspect-Based Sentiment Analysis (ABSA) aims to identify the opinion (sentiment) with respect to a specific aspect. Since there is a lack of <em>smartphone apps reviews</em> dataset that is annotated to support the ABSA task, we present AWARE: <strong>A</strong>BSA <strong>W</strong>arehouse of <strong>A</strong>pps <strong>RE</strong>views.</p> <p>AWARE contains apps reviews from three different domains (Productivity, Social Networking, and Games), as each domain has its distinct functionalities and audience. Each sentence is annotated with three labels, as follows: </p> <ul> <li><strong>Aspect Term: </strong>a term that exists in the sentence and describes an aspect of the app that is expressed by the sentiment. A term value of “N/A” means that the term is not explicitly mentioned in the sentence.</li> <li><strong>Aspect Category:</strong> one of the pre-defined set of domain-specific categories that represent an aspect of the app (e.g., security, usability, etc.).</li> <li><strong>Sentiment:</strong> positive or negative.</li> </ul> <p><em>Note: games domain does not contain aspect terms.</em></p> <p>We provide a comprehensive dataset of 11323 sentences from the three domains, where each sentence is additionally annotated with a Boolean value indicating whether the sentence expresses a positive/negative opinion. In addition, we provide three separate datasets, one for each domain, containing only sentences that express opinions. The file named “AWARE_metadata.csv” contains a description of the dataset’s columns.</p> <p><strong>How AWARE can be used?</strong></p> <p>We designed AWARE such that it can be used to serve various tasks. The tasks can be, but are not limited to:</p> <ul> <li>Sentiment Analysis.</li> <li>Aspect Term Extraction.</li> <li>Aspect Category Classification.</li> <li>Aspect Sentiment Analysis.</li> <li>Explicit/Implicit Aspect Term Classification.</li> <li>Opinion/Not-Opinion Classification.</li> </ul> <p>Furthermore, researchers can experiment with and investigate the effects of different domains on users' feedback.</p>
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 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 "iclr_conf.p" 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 "sentences.csv" contains one sentence whose corresponding annotation is provided in the corresponding line in the file "annotations.csv"<br> The file "annotations.csv" 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>
Dat4API.ABSA: A Dataset of API Reviews from Stack Overflow for Aspect-Based Sentiment Analysis
<p>This is the dataset created from Stack Overflow discussions with manually labeled<br> Aspect-API-Sentiment information, used in the paper 'Dat4API.ABSA: A Dataset of API Reviews from Stack Overflow for Aspect-Based Sentiment Analysis'.</p>
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