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word2vec.ipynb
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426
word2vec.ipynb
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{
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"cells": [
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"cell_type": "code",
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||||
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|
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"start_time": "2024-05-19T15:41:39.981058Z"
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}
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||||
},
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"outputs": [],
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"source": [
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"import gensim\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"pd.set_option('display.max_colwidth', 100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"start_time": "2024-05-19T16:06:16.000064500Z"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": " 1 \\\n0 1 \n1 0 \n2 1 \n3 0 \n4 1 \n\n Mindaugas Budzinauskas wierzy w odbudowę formy Kevina Johnsona. Czy ktoś opuści Polpharmę? Mindaugas Budzinauskas w rozmowie z WP SportoweFakty opowiada o transferze Kevina Johnsona, ewentualnych odejściach z Polpharmy i kolejnym meczu PLK z Anwilem. - Potrzebowaliśmy takiego gracza, jak Johnson - podkreśla szkoleniowiec starogardzian. \n0 Przyjmujący reprezentacji Polski wrócił do PGE Skry Bełchatów Tylko rok trwał rozbrat Artura Sza... \n1 FEN 9: Zapowiedź walki Róża Gumienna vs Katarzyna Posiadała (wideo) Podczas Fight Exclusive Nigh... \n2 Aleksander Filipiak: Czuję się dobrze w nowym klubie Aleksander Filipiak w przerwie letniej zami... \n3 Victoria Carl i Aleksiej Czerwotkin mistrzami świata juniorów na 5 i 10 kilometrów Biegi na 5 i ... \n4 Świat poznał ją na mundialu. Francuska WAG czaruje pięknym ciałem Rachel Legrain-Trapani to jedn... ",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>1</th>\n <th>Mindaugas Budzinauskas wierzy w odbudowę formy Kevina Johnsona. Czy ktoś opuści Polpharmę? Mindaugas Budzinauskas w rozmowie z WP SportoweFakty opowiada o transferze Kevina Johnsona, ewentualnych odejściach z Polpharmy i kolejnym meczu PLK z Anwilem. - Potrzebowaliśmy takiego gracza, jak Johnson - podkreśla szkoleniowiec starogardzian.</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>Przyjmujący reprezentacji Polski wrócił do PGE Skry Bełchatów Tylko rok trwał rozbrat Artura Sza...</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarzyna Posiadała (wideo) Podczas Fight Exclusive Nigh...</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n <td>Aleksander Filipiak: Czuję się dobrze w nowym klubie Aleksander Filipiak w przerwie letniej zami...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>Victoria Carl i Aleksiej Czerwotkin mistrzami świata juniorów na 5 i 10 kilometrów Biegi na 5 i ...</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1</td>\n <td>Świat poznał ją na mundialu. Francuska WAG czaruje pięknym ciałem Rachel Legrain-Trapani to jedn...</td>\n </tr>\n </tbody>\n</table>\n</div>"
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||||
},
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||||
"execution_count": 35,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
|
||||
"data = pd.read_csv(\"train.tsv\", sep=\"\\t\", on_bad_lines='skip')\n",
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||||
"data.head(5)"
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||||
]
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},
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{
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"data": {
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"text/plain": " label \\\n0 1 \n1 0 \n2 1 \n3 0 \n4 1 \n\n text \n0 Przyjmujący reprezentacji Polski wrócił do PGE Skry Bełchatów Tylko rok trwał rozbrat Artura Sza... \n1 FEN 9: Zapowiedź walki Róża Gumienna vs Katarzyna Posiadała (wideo) Podczas Fight Exclusive Nigh... \n2 Aleksander Filipiak: Czuję się dobrze w nowym klubie Aleksander Filipiak w przerwie letniej zami... \n3 Victoria Carl i Aleksiej Czerwotkin mistrzami świata juniorów na 5 i 10 kilometrów Biegi na 5 i ... \n4 Świat poznał ją na mundialu. Francuska WAG czaruje pięknym ciałem Rachel Legrain-Trapani to jedn... ",
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||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>text</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>Przyjmujący reprezentacji Polski wrócił do PGE Skry Bełchatów Tylko rok trwał rozbrat Artura Sza...</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarzyna Posiadała (wideo) Podczas Fight Exclusive Nigh...</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n <td>Aleksander Filipiak: Czuję się dobrze w nowym klubie Aleksander Filipiak w przerwie letniej zami...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>Victoria Carl i Aleksiej Czerwotkin mistrzami świata juniorów na 5 i 10 kilometrów Biegi na 5 i ...</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1</td>\n <td>Świat poznał ją na mundialu. Francuska WAG czaruje pięknym ciałem Rachel Legrain-Trapani to jedn...</td>\n </tr>\n </tbody>\n</table>\n</div>"
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},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.columns = [\"label\", \"text\"]\n",
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||||
"data.head(5)"
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]
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"text/plain": " label \\\n0 1 \n1 0 \n2 1 \n3 0 \n4 1 \n\n text \\\n0 Przyjmujący reprezentacji Polski wrócił do PGE Skry Bełchatów Tylko rok trwał rozbrat Artura Sza... \n1 FEN 9: Zapowiedź walki Róża Gumienna vs Katarzyna Posiadała (wideo) Podczas Fight Exclusive Nigh... \n2 Aleksander Filipiak: Czuję się dobrze w nowym klubie Aleksander Filipiak w przerwie letniej zami... \n3 Victoria Carl i Aleksiej Czerwotkin mistrzami świata juniorów na 5 i 10 kilometrów Biegi na 5 i ... \n4 Świat poznał ją na mundialu. Francuska WAG czaruje pięknym ciałem Rachel Legrain-Trapani to jedn... \n\n text_preprocessed \n0 [przyjmujący, reprezentacji, polski, wrócił, do, pge, skry, bełchatów, tylko, rok, trwał, rozbra... \n1 [fen, zapowiedź, walki, róża, gumienna, vs, katarzyna, posiadała, wideo, podczas, fight, exclusi... \n2 [aleksander, filipiak, czuję, się, dobrze, nowym, klubie, aleksander, filipiak, przerwie, letnie... \n3 [victoria, carl, aleksiej, czerwotkin, mistrzami, świata, juniorów, na, kilometrów, biegi, na, k... \n4 [świat, poznał, ją, na, mundialu, francuska, wag, czaruje, pięknym, ciałem, rachel, legrain, tra... ",
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"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>label</th>\n <th>text</th>\n <th>text_preprocessed</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>Przyjmujący reprezentacji Polski wrócił do PGE Skry Bełchatów Tylko rok trwał rozbrat Artura Sza...</td>\n <td>[przyjmujący, reprezentacji, polski, wrócił, do, pge, skry, bełchatów, tylko, rok, trwał, rozbra...</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>FEN 9: Zapowiedź walki Róża Gumienna vs Katarzyna Posiadała (wideo) Podczas Fight Exclusive Nigh...</td>\n <td>[fen, zapowiedź, walki, róża, gumienna, vs, katarzyna, posiadała, wideo, podczas, fight, exclusi...</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1</td>\n <td>Aleksander Filipiak: Czuję się dobrze w nowym klubie Aleksander Filipiak w przerwie letniej zami...</td>\n <td>[aleksander, filipiak, czuję, się, dobrze, nowym, klubie, aleksander, filipiak, przerwie, letnie...</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>Victoria Carl i Aleksiej Czerwotkin mistrzami świata juniorów na 5 i 10 kilometrów Biegi na 5 i ...</td>\n <td>[victoria, carl, aleksiej, czerwotkin, mistrzami, świata, juniorów, na, kilometrów, biegi, na, k...</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1</td>\n <td>Świat poznał ją na mundialu. Francuska WAG czaruje pięknym ciałem Rachel Legrain-Trapani to jedn...</td>\n <td>[świat, poznał, ją, na, mundialu, francuska, wag, czaruje, pięknym, ciałem, rachel, legrain, tra...</td>\n </tr>\n </tbody>\n</table>\n</div>"
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||||
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||||
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}
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||||
],
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||||
"source": [
|
||||
"data['text_preprocessed'] = data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))\n",
|
||||
"data.head(5)"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 39,
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||||
"metadata": {
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"end_time": "2024-05-19T16:06:55.614995200Z",
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"start_time": "2024-05-19T16:06:38.098671700Z"
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||||
}
|
||||
},
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||||
"outputs": [],
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||||
"source": [
|
||||
"from gensim.test.utils import common_texts\n",
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||||
"from gensim.models import Word2Vec\n",
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"\n",
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||||
"model = Word2Vec(sentences=data['text_preprocessed'], vector_size=100, window=5, min_count=1, workers=4)"
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||||
]
|
||||
},
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||||
{
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"cell_type": "code",
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||||
"execution_count": 40,
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||||
"metadata": {
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"end_time": "2024-05-19T16:07:14.652019200Z",
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"start_time": "2024-05-19T16:07:06.839062700Z"
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||||
}
|
||||
},
|
||||
"outputs": [],
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||||
"source": [
|
||||
"import tensorflow as tf\n",
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"\n",
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||||
"token = tf.keras.preprocessing.text.Tokenizer(1234)\n",
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||||
"token.fit_on_texts(data['text_preprocessed'])\n",
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||||
"text = token.texts_to_sequences(data['text_preprocessed'])\n",
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||||
"text = tf.keras.utils.pad_sequences(text, 75)"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 41,
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": "array([[ 0, 0, 0, ..., 189, 416, 346],\n [ 0, 0, 0, ..., 56, 397, 907],\n [ 0, 0, 0, ..., 137, 226, 343],\n ...,\n [ 0, 0, 0, ..., 817, 3, 317],\n [ 0, 0, 0, ..., 656, 20, 923],\n [ 0, 0, 0, ..., 6, 478, 27]])"
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||||
},
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||||
"execution_count": 41,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
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||||
"X_train, X_test, y_train, y_test = train_test_split(np.array(text), data['label'], test_size=0.3)\n",
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||||
"X_train"
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],
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||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
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||||
"name": "#%%\n"
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||||
},
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||||
"ExecuteTime": {
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||||
"end_time": "2024-05-19T16:07:16.936539700Z",
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||||
"start_time": "2024-05-19T16:07:16.829393500Z"
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}
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||||
}
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},
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{
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"cell_type": "code",
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"execution_count": 42,
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"outputs": [],
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"source": [
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"vocab_size = len(token.word_index) + 1\n",
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"embedding_matrix = np.zeros((vocab_size, 100))\n",
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"\n",
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"for word, i in token.word_index.items():\n",
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" if word in model.wv:\n",
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" embedding_matrix[i] = model.wv[word]\n",
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" \n",
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"import keras\n",
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"\n"
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],
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||||
"metadata": {
|
||||
"collapsed": false,
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||||
"ExecuteTime": {
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||||
"end_time": "2024-05-19T16:07:20.210193100Z",
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"start_time": "2024-05-19T16:07:19.694625200Z"
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}
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||||
}
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||||
},
|
||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 48,
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||||
"outputs": [
|
||||
{
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||||
"name": "stdout",
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"output_type": "stream",
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"text": [
|
||||
"Epoch 1/10\n",
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||||
"1074/1074 [==============================] - 45s 41ms/step - loss: 0.1928 - acc: 0.9313 - val_loss: 0.1255 - val_acc: 0.9564\n",
|
||||
"Epoch 2/10\n",
|
||||
"1074/1074 [==============================] - 43s 40ms/step - loss: 0.1985 - acc: 0.9309 - val_loss: 0.1173 - val_acc: 0.9543\n",
|
||||
"Epoch 3/10\n",
|
||||
"1074/1074 [==============================] - 44s 41ms/step - loss: 0.2170 - acc: 0.9252 - val_loss: 0.1254 - val_acc: 0.9522\n",
|
||||
"Epoch 4/10\n",
|
||||
"1074/1074 [==============================] - 44s 41ms/step - loss: 0.1953 - acc: 0.9291 - val_loss: 0.1191 - val_acc: 0.9524\n",
|
||||
"Epoch 5/10\n",
|
||||
"1074/1074 [==============================] - 44s 41ms/step - loss: 0.2065 - acc: 0.9253 - val_loss: 0.1237 - val_acc: 0.9503\n",
|
||||
"Epoch 6/10\n",
|
||||
"1074/1074 [==============================] - 43s 40ms/step - loss: 0.1990 - acc: 0.9275 - val_loss: 0.1334 - val_acc: 0.9452\n",
|
||||
"Epoch 7/10\n",
|
||||
"1074/1074 [==============================] - 44s 41ms/step - loss: 0.1960 - acc: 0.9256 - val_loss: 0.1266 - val_acc: 0.9516\n",
|
||||
"Epoch 8/10\n",
|
||||
"1074/1074 [==============================] - 45s 42ms/step - loss: 0.2203 - acc: 0.9217 - val_loss: 0.1327 - val_acc: 0.9458\n",
|
||||
"Epoch 9/10\n",
|
||||
"1074/1074 [==============================] - 43s 40ms/step - loss: 0.2149 - acc: 0.9197 - val_loss: 0.1191 - val_acc: 0.9486\n",
|
||||
"Epoch 10/10\n",
|
||||
"1074/1074 [==============================] - 69s 64ms/step - loss: 0.2136 - acc: 0.9232 - val_loss: 0.1187 - val_acc: 0.9511\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": "<keras.src.callbacks.History at 0x26e8784f9d0>"
|
||||
},
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from keras.src.layers import Dropout, Dense, Activation, Embedding, MaxPooling1D, GlobalMaxPooling1D\n",
|
||||
"from keras.src.layers import Conv1D\n",
|
||||
"from keras import Sequential\n",
|
||||
"\n",
|
||||
"opt = keras.optimizers.Adam(learning_rate=0.01)\n",
|
||||
"\n",
|
||||
"keras_model = Sequential()\n",
|
||||
"keras_model.add(Embedding(vocab_size, 100, weights=[embedding_matrix], trainable=False))\n",
|
||||
"keras_model.add(Dropout(0.4))\n",
|
||||
"keras_model.add(Conv1D(100, 3, activation='relu', padding='same', strides=1))\n",
|
||||
"keras_model.add(Conv1D(100, 3, activation='relu', padding='same', strides=1))\n",
|
||||
"keras_model.add(GlobalMaxPooling1D())\n",
|
||||
"keras_model.add(Dropout(0.4))\n",
|
||||
"keras_model.add(Dense(200))\n",
|
||||
"keras_model.add(Activation('relu'))\n",
|
||||
"keras_model.add(Dropout(0.4))\n",
|
||||
"keras_model.add(Dense(1))\n",
|
||||
"keras_model.add(Activation('sigmoid'))\n",
|
||||
"keras_model.compile(loss='binary_crossentropy', metrics=['acc'], optimizer=opt)\n",
|
||||
"keras_model.fit(X_train, y_train, batch_size=64, epochs=10, validation_data=(X_test, y_test))"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:20:23.872101800Z",
|
||||
"start_time": "2024-05-19T16:12:39.852425600Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"model = keras_model"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:23:52.063773Z",
|
||||
"start_time": "2024-05-19T16:23:52.048148Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def preprocess(path):\n",
|
||||
" data = pd.read_csv(path, sep=\"\\t\", on_bad_lines='skip')\n",
|
||||
" data.columns = [\"text\"]\n",
|
||||
" data['text_clean'] = data['text'].apply(lambda x: gensim.utils.simple_preprocess(x))\n",
|
||||
" text = token.texts_to_sequences(data['text_clean'])\n",
|
||||
" text = tf.keras.utils.pad_sequences(text, 75)\n",
|
||||
" return text"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:23:59.273586100Z",
|
||||
"start_time": "2024-05-19T16:23:59.226688700Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"171/171 [==============================] - 2s 8ms/step\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x = preprocess(\"in.tsv\")\n",
|
||||
"res = model.predict(x)\n",
|
||||
"y_predictions = np.where(res >= 0.49, 1, 0)\n",
|
||||
"out = pd.DataFrame(y_predictions)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:24:04.905985Z",
|
||||
"start_time": "2024-05-19T16:24:01.955988500Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"y_predictions = np.where(res>=0.49, 1, 0) "
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:24:25.889023300Z",
|
||||
"start_time": "2024-05-19T16:24:25.842145800Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out = pd.DataFrame(y_predictions)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:24:31.504270200Z",
|
||||
"start_time": "2024-05-19T16:24:31.410519700Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" 0\n",
|
||||
"0 1\n",
|
||||
"1 1\n",
|
||||
"2 0\n",
|
||||
"3 1\n",
|
||||
"4 1\n",
|
||||
"... ..\n",
|
||||
"5439 1\n",
|
||||
"5440 1\n",
|
||||
"5441 1\n",
|
||||
"5442 0\n",
|
||||
"5443 1\n",
|
||||
"\n",
|
||||
"[5444 rows x 1 columns]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(out)"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:24:37.105738100Z",
|
||||
"start_time": "2024-05-19T16:24:37.027601500Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"out.to_csv('out.tsv', sep=\"\\t\", index=False) "
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-05-19T16:24:46.318087Z",
|
||||
"start_time": "2024-05-19T16:24:46.239966300Z"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"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": "3.9.12"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user