Datasets:
Tasks:
Text Classification
Sub-tasks:
sentiment-classification
Languages:
Hebrew
Size:
10K<n<100K
License:
| annotations_creators: | |
| - expert-generated | |
| language_creators: | |
| - found | |
| language: | |
| - he | |
| license: | |
| - mit | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - text-classification | |
| task_ids: | |
| - sentiment-classification | |
| paperswithcode_id: modern-hebrew-sentiment-dataset | |
| pretty_name: HebrewSentiment | |
| dataset_info: | |
| - config_name: token | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': pos | |
| '1': neg | |
| '2': off-topic | |
| splits: | |
| - name: train | |
| num_bytes: 2159738 | |
| num_examples: 10244 | |
| - name: test | |
| num_bytes: 540883 | |
| num_examples: 2560 | |
| download_size: 2593643 | |
| dataset_size: 2700621 | |
| - config_name: morph | |
| features: | |
| - name: text | |
| dtype: string | |
| - name: label | |
| dtype: | |
| class_label: | |
| names: | |
| '0': pos | |
| '1': neg | |
| '2': off-topic | |
| splits: | |
| - name: train | |
| num_bytes: 2258128 | |
| num_examples: 10221 | |
| - name: test | |
| num_bytes: 571401 | |
| num_examples: 2555 | |
| download_size: 2722672 | |
| dataset_size: 2829529 | |
| # Dataset Card for HebrewSentiment | |
| ## Table of Contents | |
| - [Dataset Description](#dataset-description) | |
| - [Dataset Summary](#dataset-summary) | |
| - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
| - [Languages](#languages) | |
| - [Dataset Structure](#dataset-structure) | |
| - [Data Instances](#data-instances) | |
| - [Data Fields](#data-fields) | |
| - [Data Splits](#data-splits) | |
| - [Dataset Creation](#dataset-creation) | |
| - [Curation Rationale](#curation-rationale) | |
| - [Source Data](#source-data) | |
| - [Annotations](#annotations) | |
| - [Personal and Sensitive Information](#personal-and-sensitive-information) | |
| - [Considerations for Using the Data](#considerations-for-using-the-data) | |
| - [Social Impact of Dataset](#social-impact-of-dataset) | |
| - [Discussion of Biases](#discussion-of-biases) | |
| - [Other Known Limitations](#other-known-limitations) | |
| - [Additional Information](#additional-information) | |
| - [Dataset Curators](#dataset-curators) | |
| - [Licensing Information](#licensing-information) | |
| - [Citation Information](#citation-information) | |
| - [Contributions](#contributions) | |
| ## Dataset Description | |
| - **Homepage:** https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew | |
| - **Repository:** https://github.com/omilab/Neural-Sentiment-Analyzer-for-Modern-Hebrew | |
| - **Paper:** http://aclweb.org/anthology/C18-1190 | |
| - **Leaderboard:** | |
| - **Point of Contact:** | |
| ### Dataset Summary | |
| HebrewSentiment is a data set consists of 12,804 user comments to posts on the official Facebook page of Israel’s | |
| president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, | |
| 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, | |
| the first three months of Rivlin’s presidency.2 While the president’s posts aimed at reconciling tensions | |
| and called for tolerance and empathy, the sentiment expressed in the comments to the president’s posts | |
| was polarized between citizens who warmly thanked the president, and citizens that fiercely critiqued his | |
| policy. Of the 12,804 comments, 370 are neutral; 8,512 are positive, 3,922 negative. | |
| Data Annotation: | |
| ### Supported Tasks and Leaderboards | |
| Sentiment Analysis | |
| ### Languages | |
| Hebrew | |
| ## Dataset Structure | |
| tsv format: | |
| {hebrew_sentence}\t{sentiment_label} | |
| ### Data Instances | |
| רובי הייתי רוצה לראות ערביה נישאת ליהודי 1 | |
| תמונה יפיפיה-שפו 0 | |
| חייבים לעשות סוג של חרם כשכתבים שונאי ישראל עולים לשידור צריכים להעביר לערוץ אחר ואז תראו מה יעשה כוחו של הרייטינג ( בהקשר לדבריה של רינה מצליח ) 2 | |
| ### Data Fields | |
| - `text`: The modern hebrew inpput text. | |
| - `label`: The sentiment label. 0=positive , 1=negative, 2=off-topic. | |
| ### Data Splits | |
| | | train | test | | |
| |--------------------------|--------|---------| | |
| | HebrewSentiment (token) | 10243 | 2559 | | |
| | HebrewSentiment (morph) | 10243 | 2559 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| [More Information Needed] | |
| ### Source Data | |
| #### Initial Data Collection and Normalization | |
| User comments to posts on the official Facebook page of Israel’s | |
| president, Mr. Reuven Rivlin. In October 2015, we used the open software application Netvizz (Rieder, | |
| 2013) to scrape all the comments to all of the president’s posts in the period of June – August 2014, | |
| the first three months of Rivlin’s presidency. | |
| #### Who are the source language producers? | |
| [More Information Needed] | |
| ### Annotations | |
| #### Annotation process | |
| A trained researcher examined each comment and determined its sentiment value, | |
| where comments with an overall positive sentiment were assigned the value 0, comments with an overall | |
| negative sentiment were assigned the value 1, and comments that are off-topic to the post’s content | |
| were assigned the value 2. We validated the coding scheme by asking a second trained researcher to | |
| code the same data. There was substantial agreement between raters (N of agreements: 10623, N of | |
| disagreements: 2105, Coehn’s Kappa = 0.697, p = 0). | |
| #### Who are the annotators? | |
| Researchers | |
| ### Personal and Sensitive Information | |
| [More Information Needed] | |
| ## Considerations for Using the Data | |
| ### Social Impact of Dataset | |
| [More Information Needed] | |
| ### Discussion of Biases | |
| [More Information Needed] | |
| ### Other Known Limitations | |
| [More Information Needed] | |
| ## Additional Information | |
| ### Dataset Curators | |
| OMIlab, The Open University of Israel | |
| ### Licensing Information | |
| MIT License | |
| Copyright (c) 2018 OMIlab, The Open University of Israel | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is | |
| furnished to do so, subject to the following conditions: | |
| The above copyright notice and this permission notice shall be included in all | |
| copies or substantial portions of the Software. | |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| SOFTWARE. | |
| ### Citation Information | |
| @inproceedings{amram-etal-2018-representations, | |
| title = "Representations and Architectures in Neural Sentiment Analysis for Morphologically Rich Languages: A Case Study from {M}odern {H}ebrew", | |
| author = "Amram, Adam and | |
| Ben David, Anat and | |
| Tsarfaty, Reut", | |
| booktitle = "Proceedings of the 27th International Conference on Computational Linguistics", | |
| month = aug, | |
| year = "2018", | |
| address = "Santa Fe, New Mexico, USA", | |
| publisher = "Association for Computational Linguistics", | |
| url = "https://www.aclweb.org/anthology/C18-1190", | |
| pages = "2242--2252", | |
| abstract = "This paper empirically studies the effects of representation choices on neural sentiment analysis for Modern Hebrew, a morphologically rich language (MRL) for which no sentiment analyzer currently exists. We study two dimensions of representational choices: (i) the granularity of the input signal (token-based vs. morpheme-based), and (ii) the level of encoding of vocabulary items (string-based vs. character-based). We hypothesise that for MRLs, languages where multiple meaning-bearing elements may be carried by a single space-delimited token, these choices will have measurable effects on task perfromance, and that these effects may vary for different architectural designs {---} fully-connected, convolutional or recurrent. Specifically, we hypothesize that morpheme-based representations will have advantages in terms of their generalization capacity and task accuracy, due to their better OOV coverage. To empirically study these effects, we develop a new sentiment analysis benchmark for Hebrew, based on 12K social media comments, and provide two instances of these data: in token-based and morpheme-based settings. Our experiments show that representation choices empirical effects vary with architecture type. While fully-connected and convolutional networks slightly prefer token-based settings, RNNs benefit from a morpheme-based representation, in accord with the hypothesis that explicit morphological information may help generalize. Our endeavour also delivers the first state-of-the-art broad-coverage sentiment analyzer for Hebrew, with over 89{\%} accuracy, alongside an established benchmark to further study the effects of linguistic representation choices on neural networks{'} task performance.", | |
| } | |
| ### Contributions | |
| Thanks to [@elronbandel](https://github.com/elronbandel) for adding this dataset. |