blog/sentiment-analysis-analysis/svm.ipynb

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   "source": [
    "## Sentiment analysis Analysis Part 2— Support Vector Machines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implementation of a support vector machine classifier for the Sentiment analysis problem. <br>\n",
    "For explanation of the code, visit https://medium.com/nlpython/sentiment-analysis-analysis-part-2-support-vector-machines-31f78baeee09"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import zipfile\n",
    "with zipfile.ZipFile(\"reviews.zip\", 'r') as zip_ref:\n",
    "    zip_ref.extractall(\".\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(\"reviews.txt\") as f:\n",
    "    reviews = f.read().split(\"\\n\")\n",
    "with open(\"labels.txt\") as f:\n",
    "    labels = f.read().split(\"\\n\")\n",
    "    \n",
    "reviews_tokens = [review.split() for review in reviews]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiLabelBinarizer(classes=None, sparse_output=False)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MultiLabelBinarizer\n",
    "\n",
    "onehot_enc = MultiLabelBinarizer()\n",
    "onehot_enc.fit(reviews_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(reviews_tokens, labels, test_size=0.25, random_state=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,\n",
       "     intercept_scaling=1, loss='squared_hinge', max_iter=1000,\n",
       "     multi_class='ovr', penalty='l2', random_state=None, tol=0.0001,\n",
       "     verbose=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "lsvm = LinearSVC()\n",
    "lsvm.fit(onehot_enc.transform(X_train), y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8584\n"
     ]
    }
   ],
   "source": [
    "score = lsvm.score(onehot_enc.transform(X_test), y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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