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README.md
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This dataset contains Arabic text snippets, each labeled with a sentiment polarity (positive or negative). The data appears to be intended for tasks like sentiment analysis or text classification. It is divided into separate training and testing files (train.tsv and test.tsv).
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2. Source Files
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train.tsv
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test.tsv
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3. Language
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4. Data Format
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Tab-Separated Values (.tsv)
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Each line consists of two fields separated by a tab:
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Field 1: Sentiment Label
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Field 2: Arabic Text
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5. Features
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Sentiment Label: Categorical label indicating the polarity of the text. The observed labels are 'pos' (positive) and 'neg' (negative).
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Text: A string containing the Arabic text snippet.
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6. Instances (Estimated)
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The exact number of instances could not be determined as the full file contents were not available.
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Based on the source identifiers seen in the snippets:
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train.tsv contains approximately 1853 instances (highest source ID seen is 1853).
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test.tsv contains approximately 3142 instances (highest source ID seen is 3142).
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7. Potential Use Cases
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Training and evaluating sentiment analysis models for Arabic text.
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Text classification tasks.
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Natural Language Processing research on Arabic dialects or language variations present in the text.
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This dataset contains Arabic text snippets, each labeled with a sentiment polarity (positive or negative). The data appears to be intended for tasks like sentiment analysis or text classification. It is divided into separate training and testing files (train.tsv and test.tsv).
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2. Source Files
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- train.tsv
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- test.tsv
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3. Language
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- Arabic
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4. Data Format
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- Tab-Separated Values (.tsv)
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- Each line consists of two fields separated by a tab:
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- Field 1: Sentiment Label
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- Field 2: Arabic Text
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5. Features
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- Sentiment Label: Categorical label indicating the polarity of the text. The observed labels are 'pos' (positive) and 'neg' (negative).
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- Text: A string containing the Arabic text snippet.
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-
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6. Instances (Estimated)
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- The exact number of instances could not be determined as the full file contents were not available.
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- Based on the source identifiers seen in the snippets:
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- train.tsv contains approximately 1853 instances (highest source ID seen is 1853).
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- test.tsv contains approximately 3142 instances (highest source ID seen is 3142).
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-
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7. Potential Use Cases
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- Training and evaluating sentiment analysis models for Arabic text.
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- Text classification tasks.
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- Natural Language Processing research on Arabic dialects or language variations present in the text.
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