Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+task_categories:
+- text-classification
+language:
+- he
+---
+
+## Sentiment Analysis Data for the Hebrew Language
+
+**Dataset Description:**
+This dataset contains a sentiment analysis dataset from Amram et al. (2018).
+
+**Data Structure:**
+The data was used for the project on [improving word embeddings with graph knowledge for Low Resource Languages](https://github.com/pyRis/retrofitting-embeddings-lrls?tab=readme-ov-file).
+
+**Citation:**
+```bibtex
+@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",
+    editor = "Bender, Emily M.  and
+      Derczynski, Leon  and
+      Isabelle, Pierre",
+    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://aclanthology.org/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.",
+}
+```