{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demoji" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", "\n", "This tutorial is available as an IPython notebook at [Malaya/example/demoji](https://github.com/huseinzol05/Malaya/tree/master/example/demoji).\n", " \n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Find emojis with malay representation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load demoji\n", "\n", "Make sure you already installed `requests`,\n", "\n", "```bash\n", "pip3 install requests\n", "```" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import malaya" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "demoji = malaya.preprocessing.demoji()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Demoji\n", "\n", "```python\n", "def demoji(self, string: str):\n", " \"\"\"\n", " Find emojis with string representation.\n", " 🔥 -> emoji api.\n", "\n", " Parameters\n", " ----------\n", " string: str\n", "\n", " Returns\n", " -------\n", " result: Dist[str]\n", " \"\"\"\n", "```" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'🔥': 'api', '🙂': 'muka tersenyum sedikit'}" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "string = 'awak sangat hot ye 🔥🔥. 🔥🙂. elooo'\n", "results = demoji.demoji(string)\n", "results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Combined with malaya.preprocessing.preprocessing" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "preprocessing = malaya.preprocessing.preprocessing(demoji = demoji)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'awak sangat hot ya api api . api muka tersenyum sedikit . lo '" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "joined = ' '.join(preprocessing.process(string))\n", "for k, v in results.items():\n", " joined = joined.replace(k, v)\n", " \n", "joined" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.7" }, "varInspector": { "cols": { "lenName": 16, "lenType": 16, "lenVar": 40 }, "kernels_config": { "python": { "delete_cmd_postfix": "", "delete_cmd_prefix": "del ", "library": "var_list.py", "varRefreshCmd": "print(var_dic_list())" }, "r": { "delete_cmd_postfix": ") ", "delete_cmd_prefix": "rm(", "library": "var_list.r", "varRefreshCmd": "cat(var_dic_list()) " } }, "types_to_exclude": [ "module", "function", "builtin_function_or_method", "instance", "_Feature" ], "window_display": false } }, "nbformat": 4, "nbformat_minor": 4 }