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@ajitesh123
Created May 27, 2019 18:05
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{
"cells": [
{
"cell_type": "code",
"execution_count": 342,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"people 217\n",
"india 214\n",
"thank 196\n",
"watch. 137\n",
"congress 126\n",
"bjp 107\n",
"watch 106\n",
"rally 104\n",
"great 99\n",
"government 90\n",
"addressing 84\n",
"happy 80\n",
"best 78\n",
"forward 73\n",
"development 73\n",
"-------------------------------\n",
"\n",
"Number of times he mentioned Ram: 5\n",
"Number of times he mentioned Bengal: 27\n",
"Number of times he mentioned Bihar: 10\n",
"Number of times he mentioned Hindu: 1\n"
]
}
],
"source": [
"#Let's get word frequency \n",
"import nltk\n",
"from nltk.corpus import stopwords \n",
"stop_words = set(stopwords.words('english'))\n",
"stop_words.update([\"I\", \"The\",\"के\", \"की\",\"को\",\"में\", \"का\", \"और\",\"है।\", \"से\",\"in\", \"a\", \"ji\", \"पर\", \"shri\",\"है\", \"लिए\", \"ji.\",\n",
" \"ने\", \"also\", \"towards\"])\n",
"\n",
"counts = {}\n",
"for text in data.Tweets_Cln:\n",
" for word in text.split():\n",
" if word.lower() in stop_words:\n",
" continue\n",
" else:\n",
" counts[word.lower()]=counts.get(word.lower(),0)+1\n",
"\n",
"lst=[]\n",
"\n",
"for key,val in counts.items():\n",
" newtup=(val, key)\n",
" lst.append(newtup)\n",
"\n",
"lst=sorted(lst, reverse=True)\n",
"\n",
"count_num=[]\n",
"value=[]\n",
"for val, key in lst[:15]:\n",
" count_num.append(key)\n",
" value.append(val)\n",
" print(key, val)\n",
" \n",
"print(\"-------------------------------\")\n",
"print()\n",
"print(f\"Number of times he mentioned Ram: {counts['ram']}\")\n",
"print(f\"Number of times he mentioned Bengal: {counts['bengal']}\")\n",
"print(f\"Number of times he mentioned Bihar: {counts['bihar']}\")\n",
"print(f\"Number of times he mentioned Hindu: {counts['hindu']}\")"
]
},
{
"cell_type": "code",
"execution_count": 357,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1fe8725cb70>]"
]
},
"execution_count": 357,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1152x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"len(count_num)\n",
"plt.figure(figsize=[16,4])\n",
"plt.plot(count_num, value)"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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