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sentiment.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "cbd16dcf-4126-482e-9ceb-092e39e5c9db",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'tweepy'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtweepy\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtextblob\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TextBlob\n\u001b[1;32m      4\u001b[0m consumer_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgRgxkWG&dU*2&J@6S*3MDXoT!pJALE38V$b462Lz\u001b[39m\u001b[38;5;124m%\u001b[39m\u001b[38;5;124mjGbdPvjUe\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'tweepy'"
+     ]
+    }
+   ],
+   "source": [
+    "import tweepy\n",
+    "from textblob import TextBlob\n",
+    "\n",
+    "consumer_key = \"gRgxkWG&dU*2&J@6S*3MDXoT!pJALE38V$b462Lz%jGbdPvjUe\"\n",
+    "consumer_secret = \"cirBr#4ykVM38mD7@6jgMwmBUHzjD$8r&Rt@7Sa^vjcXau4K9$\"\n",
+    "access_token = \"vSb9wJr%RjAhhvd^$qUu3U$4d#zy6pHx#XPud$Fgu&p7hzR8p!\"\n",
+    "access_token_secret = \"gxHkNcsJL!d9^t^J3FTVApXtHLKuh2Pm3rZA#vu^!oKJH^gU5%\"\n",
+    "\n",
+    "auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n",
+    "auth.set_access_token(access_token, access_token_secret)\n",
+    "api = tweepy.API(auth)\n",
+    "\n",
+    "search_query = \"Ampel\"\n",
+    "tweet_count = 1000\n",
+    "\n",
+    "tweets = tweepy.Cursor(api.search, q=search_query, lang=\"de\").items(tweet_count)\n",
+    "\n",
+    "positiveTweets = 0\n",
+    "negativeTweets = 0\n",
+    "neutralTweets = 0\n",
+    "\n",
+    "for tweet in tweets:\n",
+    "    analysis = TextBlob(tweet.text)\n",
+    "    polarity = analysis.sentiment.polarity\n",
+    "\n",
+    "    if polarity > 0:\n",
+    "        positiveTweets += 1\n",
+    "    elif polarity < 0:\n",
+    "        negativeTweets += 1\n",
+    "    else:\n",
+    "        neutralTweets += 1\n",
+    "\n",
+    "print(search_query, tweet_count, positiveTweets, negativeTweets, neutralTweets)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}