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a6578cfdf3
* add bertopic to requirements * basic topic modeling * topic modeling using BERT; bugfix if no text on post * update for google colab * Catch connection errors * replace newline character with space * move topic analysis into PostprocessText class * set up dataflow topic analysis * expose topic model to UI * tests for class init * tests for topic analysis * more tests * take care of carriage return on windows * take care of carriage return on windows * take care of carriage return on windows * set encoding to ensure windows compatibility * track encoding error * more debug * skip topic analysis debug * windows fixes
361 строка
8.3 KiB
Plaintext
Generated
361 строка
8.3 KiB
Plaintext
Generated
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dcaa3da1",
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"metadata": {},
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"source": [
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"# Notebook for text extraction on image\n",
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"Inga Ulusoy, SSC, July 2022"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f43f327c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# if running on google colab\n",
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"# flake8-noqa-cell\n",
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"import os\n",
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"\n",
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"if \"google.colab\" in str(get_ipython()):\n",
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" # update python version\n",
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" # install setuptools\n",
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" !pip install setuptools==61 -qqq\n",
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" # install misinformation\n",
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" !pip install git+https://github.com/ssciwr/misinformation.git -qqq\n",
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" # mount google drive for data and API key\n",
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" from google.colab import drive\n",
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"\n",
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" drive.mount(\"/content/drive\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cf362e60",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"from IPython.display import Image, display\n",
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"import misinformation\n",
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"import tensorflow as tf\n",
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"\n",
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"print(tf.config.list_physical_devices(\"GPU\"))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "27675810",
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"metadata": {},
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"outputs": [],
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"source": [
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"# download the models if they are not there yet\n",
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"!python -m spacy download en_core_web_md\n",
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"!python -m textblob.download_corpora"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6da3a7aa",
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"metadata": {},
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"outputs": [],
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"source": [
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"images = misinformation.find_files(path=\"../data/all/\", limit=1000)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf811ce0",
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"metadata": {},
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"outputs": [],
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"source": [
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"for i in images[0:3]:\n",
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" display(Image(filename=i))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8b32409f",
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"metadata": {},
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"outputs": [],
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"source": [
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"mydict = misinformation.utils.initialize_dict(images[0:3])"
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]
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},
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{
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"cell_type": "markdown",
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"id": "7b8b929f",
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"metadata": {},
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"source": [
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"# google cloud vision API\n",
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"First 1000 images per month are free."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "cbf74c0b-52fe-4fb8-b617-f18611e8f986",
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"metadata": {},
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"outputs": [],
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"source": [
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"os.environ[\n",
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" \"GOOGLE_APPLICATION_CREDENTIALS\"\n",
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"] = \"../data/misinformation-campaign-981aa55a3b13.json\""
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]
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},
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{
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"cell_type": "markdown",
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"id": "0891b795-c7fe-454c-a45d-45fadf788142",
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"metadata": {},
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"source": [
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"## Inspect the elements per image"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c6ecc88",
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"metadata": {},
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"outputs": [],
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"source": [
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"misinformation.explore_analysis(mydict, identify=\"text-on-image\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "9c3e72b5-0e57-4019-b45e-3e36a74e7f52",
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"metadata": {},
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"source": [
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"## Or directly analyze for further processing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "365c78b1-7ff4-4213-86fa-6a0a2d05198f",
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"metadata": {},
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"outputs": [],
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"source": [
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"for key in mydict:\n",
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" print(key)\n",
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" mydict[key] = misinformation.text.TextDetector(\n",
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" mydict[key], analyse_text=True\n",
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" ).analyse_image()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3c063eda",
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"metadata": {},
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"source": [
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"## Convert to dataframe and write csv"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5709c2cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"outdict = misinformation.utils.append_data_to_dict(mydict)\n",
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"df = misinformation.utils.dump_df(outdict)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c4f05637",
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"metadata": {},
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"outputs": [],
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"source": [
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"# check the dataframe\n",
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"df.head(10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf6c9ddb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Write the csv\n",
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"df.to_csv(\"./data_out.csv\")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4bc8ac0a",
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"metadata": {},
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"source": [
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"# Topic analysis\n",
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"The topic analysis is carried out using [BERTopic](https://maartengr.github.io/BERTopic/index.html) using an embedded model through a [spaCy](https://spacy.io/) pipeline."
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]
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},
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{
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"cell_type": "markdown",
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"id": "4931941b",
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"metadata": {},
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"source": [
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"BERTopic takes a list of strings as input. The more items in the list, the better for the topic modeling. If the below returns an error for `analyse_topic()`, the reason can be that your dataset is too small.\n",
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"### Option 1: Use the dictionary as obtained from the above analysis."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a3450a61",
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"metadata": {},
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"outputs": [],
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"source": [
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"# make a list of all the text_english entries per analysed image from the mydict variable as above\n",
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"topic_model, topic_df, most_frequent_topics = misinformation.text.PostprocessText(\n",
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" mydict=mydict\n",
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").analyse_topic()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "95667342",
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"metadata": {},
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"source": [
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"### Option 2: Read in a csv\n",
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"Not to analyse too many images on google Cloud Vision, use the csv output to obtain the text (when rerunning already analysed images)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5530e436",
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"metadata": {},
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"outputs": [],
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"source": [
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"input_file_path = \"data_out.csv\"\n",
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"topic_model, topic_df, most_frequent_topics = misinformation.text.PostprocessText(\n",
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" use_csv=True, csv_path=input_file_path\n",
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").analyse_topic(return_topics=10)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "0b6ef6d7",
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"metadata": {},
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"source": [
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"### Access frequent topics\n",
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"A topic of `-1` stands for an outlier and should be ignored. Topic count is the number of occurence of that topic. The output is structured from most frequent to least frequent topic."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "43288cda-61bb-4ff1-a209-dcfcc4916b1f",
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"metadata": {},
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"outputs": [],
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"source": [
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"print(topic_df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "b3316770",
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"metadata": {},
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"source": [
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"### Get information for specific topic\n",
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"The most frequent topics can be accessed through `most_frequent_topics` with the most occuring topics first in the list."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "db14fe03",
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"metadata": {},
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"outputs": [],
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"source": [
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"for topic in most_frequent_topics:\n",
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" print(\"Topic:\", topic)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d10f701e",
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"metadata": {},
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"source": [
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"### Topic visualization\n",
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"The topics can also be visualized. Careful: This only works if there is sufficient data (quantity and quality)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2331afe6",
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"metadata": {},
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"outputs": [],
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"source": [
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"topic_model.visualize_topics()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "f4eaf353",
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"metadata": {},
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"source": [
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"### Save the model\n",
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"The model can be saved for future use."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e5e8377c",
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"metadata": {},
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"outputs": [],
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"source": [
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"topic_model.save(\"misinfo_posts\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c94edb9",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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},
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"vscode": {
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"interpreter": {
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"hash": "da98320027a74839c7141b42ef24e2d47d628ba1f51115c13da5d8b45a372ec2"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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