pretrained-topic-models-advantages-and-limitation.ipynb

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   "source": [
    "# Unlocking the Power of Pretrained Topic Models: Advantages, Limitations, and Considerations"
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   "source": [
    "Pretrained topic models refer to pre-built models that are already trained on large amounts of data and are available for use to analyze and categorize text data. These models have gained popularity due to their ability to save time and resources while still providing accurate and reliable results."
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    "# Advantages of Pretrained Topic Models:\n",
    "\n",
    "* Time-saving: Pretrained models save time as they are already trained and ready to use. It eliminates the need for lengthy training times, data collection, and data cleaning.\n",
    "* Accuracy: Pretrained models are trained on a large amount of data, making them more accurate than models that are trained on smaller datasets.\n",
    "* Cost-effective: Pretrained models are a cost-effective solution for companies and organizations that do not have the resources to build and train their own models.\n",
    "* Better results: Pretrained models have been tested and evaluated on a large amount of data, providing better results compared to models that have been trained on smaller datasets.\n"
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    "# Disadvantages of Pretrained Topic Models:\n",
    "\n",
    "* Limited customization: Pretrained models may not meet specific requirements or needs of the organization or individual.\n",
    "* Lack of transparency: Pretrained models are often seen as black boxes, making it difficult to understand how the model works and how it came up with its results\n",
    "* Domain-specific limitations: Pretrained models may not work well for certain domains or industries as they may be trained on a different type of data.\n"
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   "source": [
    "Pretrained topic models are a useful tool for organizations and individuals looking to analyze and categorize text data. They are a cost-effective and time-saving solution that provides accurate and reliable results. However, there are limitations to using pre-trained models, such as the lack of customization and domain-specific limitations. It is important to weigh the advantages and disadvantages of using pretrained models before deciding to use them."
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   "source": [
    "# !pip install pyLDAvis==3.4.0"
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      "/opt/conda/lib/python3.7/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n",
      "/opt/conda/lib/python3.7/site-packages/pyLDAvis/_prepare.py:248: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n",
      "  by='saliency', ascending=False).head(R).drop('saliency', 1)\n"
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       "\n",
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       "<div id=\"ldavis_el191325924838718245985419473\"></div>\n",
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\"years\", \"years\", \"years\"]}, \"R\": 30, \"lambda.step\": 0.01, \"plot.opts\": {\"xlab\": \"PC1\", \"ylab\": \"PC2\"}, \"topic.order\": [3, 8, 10, 4, 1, 2, 6, 9, 7, 5]};\n",
       "\n",
       "function LDAvis_load_lib(url, callback){\n",
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       "\n",
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       "   !function(LDAvis){\n",
       "       new LDAvis(\"#\" + \"ldavis_el191325924838718245985419473\", ldavis_el191325924838718245985419473_data);\n",
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       "   // require.js is available: use it to load d3/LDAvis\n",
       "   require.config({paths: {d3: \"https://d3js.org/d3.v5\"}});\n",
       "   require([\"d3\"], function(d3){\n",
       "      window.d3 = d3;\n",
       "      LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.2.2/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
       "        new LDAvis(\"#\" + \"ldavis_el191325924838718245985419473\", ldavis_el191325924838718245985419473_data);\n",
       "      });\n",
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       "    // require.js not available: dynamically load d3 & LDAvis\n",
       "    LDAvis_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         LDAvis_load_lib(\"https://cdn.jsdelivr.net/gh/bmabey/pyLDAvis@3.2.2/pyLDAvis/js/ldavis.v3.0.0.js\", function(){\n",
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       "            })\n",
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       "}\n",
       "</script>"
      ],
      "text/plain": [
       "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
       "topic                                                \n",
       "2     -0.131352 -0.024462       1        1  21.467360\n",
       "7     -0.147395  0.074274       2        1  13.735331\n",
       "9     -0.115033  0.100330       3        1   9.381606\n",
       "3     -0.024751  0.000362       4        1   8.990680\n",
       "0     -0.118127  0.136940       5        1   8.721223\n",
       "1      0.002126 -0.230760       6        1   8.593894\n",
       "5     -0.108630  0.152553       7        1   8.479296\n",
       "8      0.058920 -0.034067       8        1   7.832901\n",
       "6      0.080036 -0.336779       9        1   6.990991\n",
       "4      0.504205  0.161611      10        1   5.806718, topic_info=           Term          Freq         Total Category  logprob  loglift\n",
       "135          ax  63649.000000  63649.000000  Default  30.0000  30.0000\n",
       "338         edu  35681.000000  35681.000000  Default  29.0000  29.0000\n",
       "231         com  20128.000000  20128.000000  Default  28.0000  28.0000\n",
       "926  university   9444.000000   9444.000000  Default  27.0000  27.0000\n",
       "991      writes  13326.000000  13326.000000  Default  26.0000  26.0000\n",
       "..          ...           ...           ...      ...      ...      ...\n",
       "59           51     16.787082    520.451074  Topic10  -8.4737  -0.5879\n",
       "452          hi     27.976065    992.952718  Topic10  -7.9630  -0.7232\n",
       "65           80     27.615827    983.761950  Topic10  -7.9760  -0.7268\n",
       "30           25     63.949146   2522.504357  Topic10  -7.1363  -0.8288\n",
       "18           18     42.065976   1940.819797  Topic10  -7.5551  -0.9855\n",
       "\n",
       "[516 rows x 6 columns], token_table=      Topic      Freq   Term\n",
       "term                        \n",
       "0         5  0.059179     00\n",
       "0         8  0.940546     00\n",
       "1         3  0.523463    000\n",
       "1         4  0.003207    000\n",
       "1         8  0.472785    000\n",
       "...     ...       ...    ...\n",
       "996       3  0.312480  years\n",
       "996       6  0.009298  years\n",
       "996       7  0.114897  years\n",
       "996       8  0.064754  years\n",
       "996       9  0.001660  years\n",
       "\n",
       "[1475 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[3, 8, 10, 4, 1, 2, 6, 9, 7, 5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.decomposition import LatentDirichletAllocation\n",
    "import pyLDAvis.sklearn\n",
    "\n",
    "# Load dataset\n",
    "newsgroups = fetch_20newsgroups(subset='all', shuffle=True, random_state=42)\n",
    "\n",
    "# Build count vectorizer\n",
    "vectorizer = CountVectorizer(max_df=0.95, min_df=2, max_features=1000, stop_words='english')\n",
    "X = vectorizer.fit_transform(newsgroups.data)\n",
    "\n",
    "# Build LDA model\n",
    "lda = LatentDirichletAllocation(n_components=10, learning_method='online', random_state=42, max_iter=50)\n",
    "lda.fit(X)\n",
    "\n",
    "# Visualize results using pyLDAvis\n",
    "pyLDAvis.enable_notebook()\n",
    "pyLDAvis.sklearn.prepare(lda, X, vectorizer)\n"
   ]
  }
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