Commit
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9a23a8b
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Parent(s):
Update files from the datasets library (from 1.2.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.2.0
- .gitattributes +27 -0
- README.md +183 -0
- dataset_infos.json +1 -0
- dummy/1.1.0/dummy_data.zip +3 -0
- oclar.py +99 -0
.gitattributes
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README.md
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---
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annotations_creators:
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- crowdsourced
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language_creators:
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- crowdsourced
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languages:
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- ar
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licenses:
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- unknown
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multilinguality:
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- monolingual
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size_categories:
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- 1K<n<10K
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source_datasets:
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- original
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task_categories:
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- text-classification
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- text-scoring
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task_ids:
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- sentiment-classification
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- sentiment-scoring
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---
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# Dataset Card for OCLAR
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-instances)
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- [Data Splits](#data-instances)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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## Dataset Description
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- **Homepage:** [OCLAR homepage](http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#)
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- **Paper:** [paper link](https://www.semanticscholar.org/paper/Sentiment-Classifier%3A-Logistic-Regression-for-in-Omari-Al-Hajj/9319f4d9e8b3b7bfd0d214314911c071ba7ce1a0)
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- **Point of Contact:** Marwan Al Omari <marwanalomari '@' yahoo.com>
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### Dataset Summary
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The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews [Zomato website](https://www.zomato.com/lebanon)
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on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc.
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The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers
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rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451
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texts.
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### Supported Tasks and Leaderboards
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Opinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on services
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reviews, including hotels, restaurants, shops, and others.
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### Languages
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The text in the dataset is in Arabic, mainly in Lebanese (LB). The associated BCP-47 code is `ar-LB`.
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## Dataset Structure
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### Data Instances
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A typical data point comprises a `pagename` which is the name of service / location being reviewed, a `review` which is
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the review left by the user / client , and a `rating` which is a score between 1 and 5.
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The authors consider a review to be positive if the score is greater or equal than `3`, else it is considered negative.
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An example from the OCLAR data set looks as follows:
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```
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"pagename": 'Ramlet Al Baida Beirut Lebanon',
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"review": 'مكان يطير العقل ويساعد على الاسترخاء',
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"rating": 5,
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```
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### Data Fields
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- `pagename`: string name of the service / location being reviewed
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- `review`: string review left by the user / costumer
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- `rating`: number of stars left by the reviewer. It ranges from 1 to 5.
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### Data Splits
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The data set comes in a single csv file of a total `3916` reviews :
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- `3465` are considered positive (a rating of 3 to 5)
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- `451` are considered negative (a rating of 1 or 2)
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## Dataset Creation
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### Curation Rationale
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This dataset was created for Arabic sentiment classification on services’ reviews in Lebanon country.
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Reviews are about public services, including hotels, restaurants, shops, and others.
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### Source Data
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#### Initial Data Collection and Normalization
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The data was collected from Google Reviews and [Zomato website](https://www.zomato.com/lebanon)
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#### Who are the source language producers?
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The source language producers are people who posted their reviews on Google Reviews or [Zomato website](https://www.zomato.com/lebanon).
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They're mainly Arabic speaking Lebanese people.
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### Annotations
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#### Annotation process
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The dataset does not contain any additional annotations
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#### Who are the annotators?
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[More Information Needed]
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### Personal and Sensitive Information
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[More Information Needed]
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## Considerations for Using the Data
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### Social Impact of Dataset
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The author's research has tackled a highly important task of sentiment analysis for Arabic language in the Lebanese
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context on 3916 reviews’ services from Google and Zomato. Experiments show three main findings:
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1) The classifier is confident when used to predict positive reviews,
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2) while it is biased on predicting reviews with negative sentiment, and finally
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3) the low percentage of negative reviews in the corpus contributes to the diffidence of LR.
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### Discussion of Biases
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[More Information Needed]
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### Other Known Limitations
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[More Information Needed]
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## Additional Information
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### Dataset Curators
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This dataset was curated by Marwan Al Omari, Moustafa Al-Hajj from Centre for Language Sciences and Communication,
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Lebanese University, Beirut, Lebanon; Nacereddine Hammami from college of Computer and Information Sciences,
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Jouf University, Aljouf, KSA; and Amani Sabra from Centre for Language Sciences and Communication, Lebanese University,
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Beirut, Lebanon.
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### Licensing Information
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[More Information Needed]
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### Citation Information
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- Marwan Al Omari, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, marwanalomari '@' yahoo.com
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- Moustafa Al-Hajj, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, moustafa.alhajj '@' ul.edu.lb
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- Nacereddine Hammami, college of Computer and Information Sciences, Jouf University, Aljouf, KSA, n.hammami '@' ju.edu.sa
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- Amani Sabra, Centre for Language Sciences and Communication, Lebanese University, Beirut, Lebanon, amani.sabra '@' ul.edu.lb
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```
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@misc{Dua:2019 ,
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author = "Dua, Dheeru and Graff, Casey",
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year = "2017",
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title = "{UCI} Machine Learning Repository",
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url = "http://archive.ics.uci.edu/ml",
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institution = "University of California, Irvine, School of Information and Computer Sciences" }
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@InProceedings{AlOmari2019oclar,
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title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},
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authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},
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year={2019}
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}
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```
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dataset_infos.json
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{"default": {"description": "The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato website \n(https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops, etc.\nThe corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers\nrating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about 451 texts.\n", "citation": "\n@misc{Dua:2019 ,\nauthor = \"Dua, Dheeru and Graff, Casey\",\nyear = \"2017\",\ntitle = \"{UCI} Machine Learning Repository\",\nurl = \"http://archive.ics.uci.edu/ml\",\ninstitution = \"University of California, Irvine, School of Information and Computer Sciences\" }\n\n@InProceedings{AlOmari2019oclar,\ntitle = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},\nauthors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},\nyear={2019}\n}\n", "homepage": "http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#", "license": "", "features": {"pagename": {"dtype": "string", "id": null, "_type": "Value"}, "review": {"dtype": "string", "id": null, "_type": "Value"}, "rating": {"dtype": "int8", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "oclar", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 398204, "num_examples": 3916, "dataset_name": "oclar"}}, "download_checksums": {"http://archive.ics.uci.edu/ml/machine-learning-databases/00499/OCLAR%20-%20Opinion%20Corpus%20for%20Lebanese%20Arabic%20Reviews.csv": {"num_bytes": 382976, "checksum": "78e0bc3d5accd737d3df45297f3f384a0238e2aa81006d546fa2cda92de12197"}}, "download_size": 382976, "post_processing_size": null, "dataset_size": 398204, "size_in_bytes": 781180}}
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dummy/1.1.0/dummy_data.zip
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version https://git-lfs.github.com/spec/v1
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oid sha256:c37690e3614177b10ab5b45712de3066ee6c01a31b7997e365216aa539fd55eb
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size 807
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oclar.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Opinion Corpus for Lebanese Arabic Reviews (OCLAR) Data Set"""
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from __future__ import absolute_import, division, print_function
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import csv
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import datasets
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_CITATION = """
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@misc{Dua:2019 ,
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author = "Dua, Dheeru and Graff, Casey",
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year = "2017",
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title = "{UCI} Machine Learning Repository",
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url = "http://archive.ics.uci.edu/ml",
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institution = "University of California, Irvine, School of Information and Computer Sciences" }
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@InProceedings{AlOmari2019oclar,
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title = {Sentiment Classifier: Logistic Regression for Arabic Services Reviews in Lebanon},
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authors={Al Omari, M., Al-Hajj, M., Hammami, N., & Sabra, A.},
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year={2019}
|
36 |
+
}
|
37 |
+
"""
|
38 |
+
|
39 |
+
_DESCRIPTION = """\
|
40 |
+
The researchers of OCLAR Marwan et al. (2019), they gathered Arabic costumer reviews from Google reviewsa and Zomato
|
41 |
+
website (https://www.zomato.com/lebanon) on wide scope of domain, including restaurants, hotels, hospitals, local shops,
|
42 |
+
etc.The corpus finally contains 3916 reviews in 5-rating scale. For this research purpose, the positive class considers
|
43 |
+
rating stars from 5 to 3 of 3465 reviews, and the negative class is represented from values of 1 and 2 of about
|
44 |
+
451 texts.
|
45 |
+
"""
|
46 |
+
|
47 |
+
_HOMEPAGE = "http://archive.ics.uci.edu/ml/datasets/Opinion+Corpus+for+Lebanese+Arabic+Reviews+%28OCLAR%29#"
|
48 |
+
|
49 |
+
# TODO: Add the licence for the dataset here if you can find it
|
50 |
+
_LICENSE = ""
|
51 |
+
|
52 |
+
_URL = "http://archive.ics.uci.edu/ml/machine-learning-databases/00499/OCLAR%20-%20Opinion%20Corpus%20for%20Lebanese%20Arabic%20Reviews.csv"
|
53 |
+
|
54 |
+
|
55 |
+
class Oclar(datasets.GeneratorBasedBuilder):
|
56 |
+
"""TOpinion Corpus for Lebanese Arabic Reviews (OCLAR) corpus is utilizable for Arabic sentiment classification on
|
57 |
+
services reviews, including hotels, restaurants, shops, and others.
|
58 |
+
"""
|
59 |
+
|
60 |
+
VERSION = datasets.Version("1.1.0")
|
61 |
+
|
62 |
+
def _info(self):
|
63 |
+
return datasets.DatasetInfo(
|
64 |
+
description=_DESCRIPTION,
|
65 |
+
features=datasets.Features(
|
66 |
+
{
|
67 |
+
"pagename": datasets.Value("string"),
|
68 |
+
"review": datasets.Value("string"),
|
69 |
+
"rating": datasets.Value("int8"),
|
70 |
+
}
|
71 |
+
),
|
72 |
+
supervised_keys=None,
|
73 |
+
homepage=_HOMEPAGE,
|
74 |
+
license=_LICENSE,
|
75 |
+
citation=_CITATION,
|
76 |
+
)
|
77 |
+
|
78 |
+
def _split_generators(self, dl_manager):
|
79 |
+
"""Returns SplitGenerators."""
|
80 |
+
data_path = dl_manager.download_and_extract(_URL)
|
81 |
+
return [
|
82 |
+
datasets.SplitGenerator(
|
83 |
+
name=datasets.Split.TRAIN,
|
84 |
+
gen_kwargs={
|
85 |
+
"filepath": data_path,
|
86 |
+
"split": "train",
|
87 |
+
},
|
88 |
+
)
|
89 |
+
]
|
90 |
+
|
91 |
+
def _generate_examples(self, filepath, split):
|
92 |
+
""" Yields examples. """
|
93 |
+
with open(filepath, encoding="utf-8") as csv_file:
|
94 |
+
csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
|
95 |
+
next(csv_reader, None) # skipping headers
|
96 |
+
for id_, row in enumerate(csv_reader):
|
97 |
+
pagename, review, rating = row
|
98 |
+
rating = int(rating)
|
99 |
+
yield id_, {"pagename": pagename, "review": review, "rating": rating}
|