Liwc_Sentiment_Prediction.ipynb

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   "cell_type": "code",
   "execution_count": 10,
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   "source": [
    "import pandas as pd\n",
    "\n",
    "posts = pd.read_csv('post_img_polarity.csv.gz')\n",
    "posts['img_polarity' ] = posts['img_polarity'].values.astype(int)\n",
    "posts = posts[posts['img_polarity'] >= 0]\n",
    "posts = posts[posts['wc'] >= 10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = posts['img_polarity'].values\n",
    "data = posts.drop(['img_polarity','post.id', 'Unnamed: 0', 'Unnamed: 0.1', 'img', 'blog.id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "0.3623940623538551"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "model = RandomForestClassifier(n_jobs=4)\n",
    "scores = cross_val_score(model, data, target, cv=5, scoring='f1_micro')\n",
    "np.mean(scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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