analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "sns.set_style('darkgrid')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df = pd.DataFrame(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/publications.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop_duplicates(\"doi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "ax = df[\"year\"].value_counts().sort_index(ascending=False).plot(kind=\"bar\")\n",
    "n = df[\"year\"].count()\n",
    "ax.set_ylabel(\"# publication\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_title(f\"Year of publication \\t ($n={n}$)\")\n",
    "_ = ax.set_xticklabels(ax.get_xticklabels(), rotation=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022    148\n",
       "2021    139\n",
       "2020     78\n",
       "2023     36\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"year\"].value_counts()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "ax = df[\"acronym\"].value_counts().sort_index(ascending=False).plot(kind=\"bar\")\n",
    "n = df[\"acronym\"].count()\n",
    "ax.set_ylabel(\"# publication\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_title(f\"Conference of publication \\t ($n={n}$)\") \n",
    "_ = ax.set_xticklabels(ax.get_xticklabels(), rotation=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    401.000000\n",
       "mean       4.985037\n",
       "std        4.191035\n",
       "min        1.000000\n",
       "25%        3.000000\n",
       "50%        4.000000\n",
       "75%        6.000000\n",
       "max       36.000000\n",
       "Name: num_authors, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"num_authors\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13997/427700068.py:8: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  _ = ax.set_xticklabels(ax.get_xticklabels(), rotation=0)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams.update({'font.size': 14})\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "ax = df[\"num_authors\"].value_counts().sort_index().plot(kind=\"line\")\n",
    "n = df[\"num_authors\"].count()\n",
    "ax.set_ylabel(\"# publication\")\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_title(f\"Number of authors \\t ($n={n}$)\") \n",
    "_ = ax.set_xticklabels(ax.get_xticklabels(), rotation=0)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Tf-IDF topics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import SnowballStemmer, PorterStemmer\n",
    "from nltk.corpus import stopwords\n",
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /home/jueri/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_punctuation(text):\n",
    "    text = re.sub(r'[^\\w\\s]', '', text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_13997/4000913407.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"title_clean\"] = df[\"title\"].apply(remove_punctuation)\n"
     ]
    }
   ],
   "source": [
    "df[\"title_clean\"] = df[\"title\"].apply(remove_punctuation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = df.groupby(\"acronym\").agg({\"title_clean\": \" \".join})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer('english')\n",
    "# Initialize Porter stemmer\n",
    "stemmer = PorterStemmer()\n",
    "\n",
    "def custom_tokenizer(text):\n",
    "    words = word_tokenize(text)\n",
    "\n",
    "    # Stem words\n",
    "    stemmed_words = [stemmer.stem(word) for word in words]\n",
    "    \n",
    "    # Remove stopwords\n",
    "    filtered_words = [word for word in stemmed_words if word not in stopwords.words('english')]\n",
    "    \n",
    "    # Create bi-grams\n",
    "    bi_grams = [' '.join(bi) for bi in zip(filtered_words, filtered_words[1:])]\n",
    "    \n",
    "    return filtered_words + bi_grams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(tokenizer=custom_tokenizer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jueri/miniconda3/envs/geo/lib/python3.8/site-packages/sklearn/feature_extraction/text.py:528: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "tfidf_matrix = vectorizer.fit_transform(docs[\"title_clean\"].tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['10th', '10th intern', '11th', ..., 'xvision explain', 'year',\n",
       "       'year insid'], dtype=object)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer.get_feature_names_out()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = pd.concat([docs, tfidf_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs.set_index(\"acronym\", inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "docs = docs.drop(\"title_clean\", axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_100 = {}\n",
    "for c in docs.T.columns:\n",
    "    top = docs.T[c].sort_values(ascending=False)[:100].index.tolist()\n",
    "    top = [x for x in top if not x.isnumeric()]\n",
    "    top_100[c] = top[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>CHIIR</th>\n",
       "      <th>CIKM</th>\n",
       "      <th>CLEF</th>\n",
       "      <th>ECIR</th>\n",
       "      <th>SIGIR</th>\n",
       "      <th>WWW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>search</td>\n",
       "      <td>graph</td>\n",
       "      <td>overview</td>\n",
       "      <td>retriev</td>\n",
       "      <td>retriev</td>\n",
       "      <td>graph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>user</td>\n",
       "      <td>knowledg graph</td>\n",
       "      <td>lab</td>\n",
       "      <td>bir</td>\n",
       "      <td>answer</td>\n",
       "      <td>knowledg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>influenc</td>\n",
       "      <td>knowledg</td>\n",
       "      <td>retriev</td>\n",
       "      <td>overview</td>\n",
       "      <td>convers question</td>\n",
       "      <td>knowledg graph</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>strategi</td>\n",
       "      <td>learn</td>\n",
       "      <td>detect</td>\n",
       "      <td>detect</td>\n",
       "      <td>learn</td>\n",
       "      <td>use</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>engin</td>\n",
       "      <td>data</td>\n",
       "      <td>retriev overview</td>\n",
       "      <td>lab</td>\n",
       "      <td>question answer</td>\n",
       "      <td>network</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>search engin</td>\n",
       "      <td>semant</td>\n",
       "      <td>fake</td>\n",
       "      <td>inform</td>\n",
       "      <td>convers</td>\n",
       "      <td>data</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>interact</td>\n",
       "      <td>use</td>\n",
       "      <td>verif</td>\n",
       "      <td>evalu</td>\n",
       "      <td>question</td>\n",
       "      <td>link</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>privaci</td>\n",
       "      <td>network</td>\n",
       "      <td>authorship verif</td>\n",
       "      <td>rank</td>\n",
       "      <td>web</td>\n",
       "      <td>social</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>inform</td>\n",
       "      <td>machin</td>\n",
       "      <td>authorship</td>\n",
       "      <td>fake</td>\n",
       "      <td>answer heterogen</td>\n",
       "      <td>web</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>learn</td>\n",
       "      <td>link</td>\n",
       "      <td>fake news</td>\n",
       "      <td>style</td>\n",
       "      <td>sourc</td>\n",
       "      <td>learn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>convers</td>\n",
       "      <td>model</td>\n",
       "      <td>academ</td>\n",
       "      <td>search</td>\n",
       "      <td>recommend</td>\n",
       "      <td>media</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>retriev</td>\n",
       "      <td>retriev</td>\n",
       "      <td>patent overview</td>\n",
       "      <td>use</td>\n",
       "      <td>search</td>\n",
       "      <td>extract</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>reus</td>\n",
       "      <td>spatiotempor</td>\n",
       "      <td>academ search</td>\n",
       "      <td>news</td>\n",
       "      <td>rank</td>\n",
       "      <td>user</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>resourc</td>\n",
       "      <td>classif</td>\n",
       "      <td>overview chemu</td>\n",
       "      <td>bibliometricenhanc</td>\n",
       "      <td>heterogen</td>\n",
       "      <td>servic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>queri</td>\n",
       "      <td>machin learn</td>\n",
       "      <td>news</td>\n",
       "      <td>bibliometricenhanc inform</td>\n",
       "      <td>graph</td>\n",
       "      <td>model</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>web</td>\n",
       "      <td>base</td>\n",
       "      <td>touché</td>\n",
       "      <td>bir workshop</td>\n",
       "      <td>semant</td>\n",
       "      <td>toward</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>languag queri</td>\n",
       "      <td>dataset</td>\n",
       "      <td>chemu</td>\n",
       "      <td>intern bir</td>\n",
       "      <td>ir</td>\n",
       "      <td>inform</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>role</td>\n",
       "      <td>system</td>\n",
       "      <td>style</td>\n",
       "      <td>social</td>\n",
       "      <td>workshop</td>\n",
       "      <td>open</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>multimod search</td>\n",
       "      <td>entiti</td>\n",
       "      <td>style chang</td>\n",
       "      <td>applic</td>\n",
       "      <td>heterogen sourc</td>\n",
       "      <td>represent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>chiir</td>\n",
       "      <td>contrast</td>\n",
       "      <td>argument retriev</td>\n",
       "      <td>text</td>\n",
       "      <td>log</td>\n",
       "      <td>onlin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              CHIIR            CIKM              CLEF  \\\n",
       "0            search           graph          overview   \n",
       "1              user  knowledg graph               lab   \n",
       "2          influenc        knowledg           retriev   \n",
       "3          strategi           learn            detect   \n",
       "4             engin            data  retriev overview   \n",
       "5      search engin          semant              fake   \n",
       "6          interact             use             verif   \n",
       "7           privaci         network  authorship verif   \n",
       "8            inform          machin        authorship   \n",
       "9             learn            link         fake news   \n",
       "10          convers           model            academ   \n",
       "11          retriev         retriev   patent overview   \n",
       "12             reus    spatiotempor     academ search   \n",
       "13          resourc         classif    overview chemu   \n",
       "14            queri    machin learn              news   \n",
       "15              web            base            touché   \n",
       "16    languag queri         dataset             chemu   \n",
       "17             role          system             style   \n",
       "18  multimod search          entiti       style chang   \n",
       "19            chiir        contrast  argument retriev   \n",
       "\n",
       "                         ECIR             SIGIR             WWW  \n",
       "0                     retriev           retriev           graph  \n",
       "1                         bir            answer        knowledg  \n",
       "2                    overview  convers question  knowledg graph  \n",
       "3                      detect             learn             use  \n",
       "4                         lab   question answer         network  \n",
       "5                      inform           convers            data  \n",
       "6                       evalu          question            link  \n",
       "7                        rank               web          social  \n",
       "8                        fake  answer heterogen             web  \n",
       "9                       style             sourc           learn  \n",
       "10                     search         recommend           media  \n",
       "11                        use            search         extract  \n",
       "12                       news              rank            user  \n",
       "13         bibliometricenhanc         heterogen          servic  \n",
       "14  bibliometricenhanc inform             graph           model  \n",
       "15               bir workshop            semant          toward  \n",
       "16                 intern bir                ir          inform  \n",
       "17                     social          workshop            open  \n",
       "18                     applic   heterogen sourc       represent  \n",
       "19                       text               log           onlin  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(top_100)"
   ]
  }
 ],
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