C_Breast_Cancer_Concern_Identification.ipynb

{"cells":[{"cell_type":"markdown","metadata":{},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/swati-rajwal/BreastCancer_tweets_project/blob/main/C_Breast_Cancer_Concern_Identification.ipynb)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":17224,"status":"ok","timestamp":1711852810955,"user":{"displayName":"Avinash kumar Pandey","userId":"07918242858559780186"},"user_tz":240},"id":"aUmhOuY4Pd9s","outputId":"dd75f1f8-580a-4929-831f-31139bf41aad"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/drive\n"]}],"source":["# Connecting the Python Code with the google drive\n","from google.colab import drive\n","drive.mount(\"/content/drive\")"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":149218,"status":"ok","timestamp":1711852962517,"user":{"displayName":"Avinash kumar Pandey","userId":"07918242858559780186"},"user_tz":240},"id":"PidBzbrdn9w-","outputId":"2e5085cc-e89d-4413-c359-81ad728e99de"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting bertopic\n","  Downloading bertopic-0.16.0-py2.py3-none-any.whl (154 kB)\n","\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/154.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m153.6/154.1 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m154.1/154.1 kB\u001b[0m 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umap-learn\n","Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, cython, nvidia-cusparse-cu12, nvidia-cudnn-cu12, pynndescent, nvidia-cusolver-cu12, hdbscan, umap-learn, sentence-transformers, bertopic\n","  Attempting uninstall: cython\n","    Found existing installation: Cython 3.0.9\n","    Uninstalling Cython-3.0.9:\n","      Successfully uninstalled Cython-3.0.9\n","Successfully installed bertopic-0.16.0 cython-0.29.37 hdbscan-0.8.33 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 pynndescent-0.5.12 sentence-transformers-2.6.1 umap-learn-0.5.5\n"]}],"source":["!pip install bertopic"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":248,"status":"ok","timestamp":1711853937338,"user":{"displayName":"Avinash kumar Pandey","userId":"07918242858559780186"},"user_tz":240},"id":"V4G_FNcMlUHf","outputId":"89bb97fc-fde2-45b8-eaf3-00b806850460"},"outputs":[{"name":"stderr","output_type":"stream","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Package punkt is already up-to-date!\n","[nltk_data] Downloading package stopwords to /root/nltk_data...\n","[nltk_data]   Package stopwords is already up-to-date!\n"]}],"source":["import pandas as pd\n","import re\n","import string\n","from bertopic import BERTopic\n","import gensim\n","from gensim.utils import simple_preprocess\n","import nltk\n","nltk.download('punkt')\n","nltk.download('stopwords')\n","import string\n","from nltk.corpus import stopwords\n","from nltk.tokenize import word_tokenize\n","from gensim.models import KeyedVectors\n","from sklearn.metrics.pairwise import cosine_similarity\n","import numpy as np"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bobmL1KwPeXT"},"outputs":[],"source":["# Load pre-trained Word2Vec embeddings\n","word_vectors = KeyedVectors.load_word2vec_format('/content/drive/MyDrive/Emory/BMI NLP/Breast Cancer Data/GoogleNews-vectors-negative300.bin', binary=True)\n","\n","# ITERATION 1: GPT-4 based list of uncertainty words\n","gpt4_concerns_keywords = [\n","    \"uncertain\", \"doubtful\", \"ambiguous\", \"hesitant\", \"perplexed\", \"confused\",\"questionable\", \"unsure\", \"indecisive\", \"bewildered\", \"vague\", \"puzzled\",\n","    \"skeptical\", \"inconclusive\", \"wavering\", \"distrustful\", \"baffled\", \"ambivalent\", \"hazy\", \"tentative\"]\n","\n","# ITERATION 2: Human Analysis of Random Posts + GPT assisted Keywords from Iteration 1\n","gpt4_human_concerns_iteration1_keywords = [\"terrified\", \"scared\", \"nervous\", \"suspicion\",\"uncertain\",\"hesitant\",\"perplexed\" ,\"clueless\"\n","              , \"worried\", \"alarmed\", \"anxiety\", \"unsure\",\"puzzled\", \"skeptical\", \"baffled\",\n","              \"doubtful\", \"unaware\" , \"awkward\", \"paranoid\", \"myth\", \"confused\", \"embarrassed\"]\n","\n","# ITERATION 3: Final Set of Keywords (GPT 4 + Human Assisted Concerns Keywords) + Keywords related to prominent topic modeling of previous iteration\n","final_concerns_keywords = [\"scared\", \"nervous\",\"worried\", \"suspicion\",\"uncertain\",\"reluctant\" ,\"confused\", \"doubtful\",\n","               \"unsure\",\"skeptical\", \"awkward\", \"insurance\", \"soy\", \"covid\", \"black\", \"bra\",\"concerned\"]\n","\n","# Function to find similar words in a text\n","def find_similar_words(text, word_vectors, uncertainty_words, threshold=0.5):\n","    similar_words = []\n","    unc_words = []\n","    for word in text.lower().split():\n","        if word in word_vectors:\n","            word_embedding = word_vectors[word]\n","            for unc_word in uncertainty_words:\n","                if unc_word in word_vectors:\n","                    unc_embedding = word_vectors[unc_word]\n","                    similarity = cosine_similarity([word_embedding], [unc_embedding])[0][0]\n","                    if similarity > threshold:\n","                        similar_words.append(word)\n","                        similar_words.append(unc_word)\n","                        break\n","    return similar_words\n","# Load the dataset\n","df = pd.read_csv(\"/content/drive/MyDrive/Emory/BMI NLP/Breast Cancer Data/Classification_ data/test_data_2.csv\")\n","df['similar_uncertainty_words'] = df['text'].apply(lambda x: find_similar_words(x, word_vectors, final_concerns_keywords))\n","df.to_csv('/content/drive/MyDrive/Emory/BMI NLP/Breast Cancer Data/CSV Files/concerns_test_data2.csv', encoding='utf-8', index=False)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1051,"status":"ok","timestamp":1711853946795,"user":{"displayName":"Avinash kumar Pandey","userId":"07918242858559780186"},"user_tz":240},"id":"_Eheh_AIXrPX","outputId":"4f9c6789-7d32-4886-9e14-f3eb30a8e96d"},"outputs":[{"name":"stdout","output_type":"stream","text":["Shape of df_full:  (83695, 5)\n","Shape of df_full with keywords matched:  (7863, 5)\n"]}],"source":["# Dataset Statistic\n","df_full = pd.read_csv('/content/drive/MyDrive/Emory/BMI NLP/Breast Cancer Data/CSV Files/concerns_test_data2.csv.csv')\n","print(\"Shape of df_full: \", df_full.shape)\n","df_full = df_full[df_full['similar_uncertainty_words']!='[]']\n","df_full.dropna(inplace=True)\n","print(\"Shape of df_full with keywords matched: \", df_full.shape)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":188,"referenced_widgets":["f64b489419aa442e95b091f93646044f","fee846fd16d749b2b6d66d60ce7e9703","6d2cbedd7f834f13a7fcef4ee013b485","54952564196a470091c6e2c836c2d48e","110865bc2baf47cd9231984dcf06df60","9626dd6cee3a41a583fda939dda3b6f7","c1d3821154e44b8d813c2c59535a372c","9954455f207a447f92461086907e9d8c","fba615cf7cba4c5ab1ea30857a42cba1","442dc72fce684fe1aca9a7bc904cf61b","c4229282775544c1ba8a1fe7be0c513a"]},"executionInfo":{"elapsed":54034,"status":"ok","timestamp":1711854005500,"user":{"displayName":"Avinash kumar Pandey","userId":"07918242858559780186"},"user_tz":240},"id":"u8h5FIVZX1JS","outputId":"94bab29a-674a-4a62-f3b1-6d682f4acd65"},"outputs":[{"name":"stderr","output_type":"stream","text":["2024-03-31 02:59:13,121 - BERTopic - Embedding - Transforming documents to embeddings.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f64b489419aa442e95b091f93646044f","version_major":2,"version_minor":0},"text/plain":["Batches:   0%|          | 0/246 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["2024-03-31 02:59:52,219 - BERTopic - Embedding - Completed ✓\n","2024-03-31 02:59:52,221 - BERTopic - Dimensionality - Fitting the dimensionality reduction algorithm\n","2024-03-31 03:00:04,163 - BERTopic - Dimensionality - Completed ✓\n","2024-03-31 03:00:04,165 - BERTopic - Cluster - Start clustering the reduced embeddings\n","2024-03-31 03:00:04,585 - BERTopic - Cluster - Completed ✓\n","2024-03-31 03:00:04,591 - BERTopic - Representation - Extracting topics from clusters using representation models.\n","2024-03-31 03:00:05,032 - BERTopic - Representation - Completed ✓\n"]}],"source":["# Pre-load the stopwords\n","STOPWORDS = set(stopwords.words('english'))\n","def remove_emojis(text):\n","    # Regular expression to match emojis\n","    emoji_pattern = re.compile(\"[\"\n","                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n","                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n","                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n","                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n","                           \"]+\", flags=re.UNICODE)\n","    return emoji_pattern.sub(r'', text)\n","def preprocess_text_optimized(text):\n","    # text = remove_emojis(text)\n","    text = text.lower()\n","    # Remove <word> patterns\n","    text = re.sub(r'<[^>]+>', '', text)\n","    text = text.translate(str.maketrans('', '', string.punctuation))\n","    text = text.replace('@', '').replace('\\n', '')\n","    tokens = word_tokenize(text)\n","    # Filter out stopwords\n","    filtered_text = [word for word in tokens if word not in STOPWORDS]\n","    return ' '.join(filtered_text)\n","\n","# Apply the preprocess function to the DataFrame column\n","df_full['text'] = df_full['text'].apply(preprocess_text_optimized)\n","tweets = df_full['text'].to_list()\n","topic_model = BERTopic(verbose=True)\n","topics, probs = topic_model.fit_transform(tweets)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1072,"status":"ok","timestamp":1701061037386,"user":{"displayName":"Swati Rajwal","userId":"15111970205131599335"},"user_tz":300},"id":"MmoolTSxwNi7","outputId":"536d5800-7be6-4a13-b45f-7194cbc5b197"},"outputs":[{"data":{"text/html":["<html>\n","<head><meta charset=\"utf-8\" /></head>\n","<body>\n","    <div>            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) 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