2. Sentiment Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "(1) Collect sample data form Google News with keyword \"ChatGPT\" snd \"Diffussion models\", save them into a Excel file as first colum"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "3-PKpsD2-KLc"
      },
      "outputs": [],
      "source": [
        "import requests\n",
        "from bs4 import BeautifulSoup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jXMAX_7r-N0w"
      },
      "outputs": [],
      "source": [
        "def get_title(url, block_element, cls, num):\n",
        "    # Send a request to the search page with given url\n",
        "    page = requests.get(url)\n",
        "    soup = BeautifulSoup(page.text, 'html.parser')\n",
        "    mycls = soup.find_all(block_element, class_= cls)\n",
        "    i = 1\n",
        "    title_list = []\n",
        "    for element in mycls:\n",
        "        if i <= num:\n",
        "            title_list.append(element.text) \n",
        "            i += 1\n",
        "        else:\n",
        "            break\n",
        "    return title_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "9XEeCHOH-N7P"
      },
      "outputs": [],
      "source": [
        "gpt_list = get_title('https://news.google.com/search?q=ChatGPT&hl=en-US&gl=US&ceid=US%3Aen', \n",
        "                     'a', 'DY5T1d RZIKme', 10)\n",
        "dm_list = get_title('https://news.google.com/search?q=Diffusion%20models&hl=en-US&gl=US&ceid=US%3Aen', \n",
        "                    'a', 'DY5T1d RZIKme', 10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "HE3kgfdW-N9s"
      },
      "outputs": [],
      "source": [
        "title_list = gpt_list + dm_list"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VWjevdoE9Zs",
        "outputId": "060fbaa4-1ab7-41d3-c1e7-dc0844c7402c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['300 million jobs could be affected by latest wave of AI, says Goldman Sachs',\n",
              " 'In Sudden Alarm, Tech Doyens Call for a Pause on ChatGPT',\n",
              " 'AI Leaders Urge Labs to Halt Training Models More Powerful Than ChatGPT-4',\n",
              " 'Musk, scientists call for halt to AI race sparked by ChatGPT',\n",
              " \"Japan PM Quizzed by ChatGPT as Lawmaker Enlists Bot's Help\",\n",
              " 'ChatGPT Made Me Cry and Other Adventures in AI Land',\n",
              " 'AI ChatGPT Chatbot Related Prompt Engineer Jobs Pay Up to $335,000',\n",
              " 'Google denies Bard was trained with ChatGPT data',\n",
              " 'How ChatGPT and Bard Performed as My Executive Assistants',\n",
              " 'ChatGPT vs. Google Bard vs. Bing Chat: Which generative AI solution is best?',\n",
              " 'Diffusion models can be contaminated with backdoors, study finds',\n",
              " 'Meet Web Stable Diffusion: An AI Project That Brings Stable Diffusion Models To Web Browsers',\n",
              " 'Meet Pix2Video: A Training-Free And Text-Guided AI Approach That Simplifies Video Editing Using Image Diffusion Models',\n",
              " 'Researchers From Stanford Introduce Locally Conditioned Diffusion: A Method For Compositional Text-To-Image Generation Using Diffusion Models',\n",
              " 'One Diffusion to Rule Diffusion: Modulating Pre-trained Diffusion Models for Multimodal Image Synthesis',\n",
              " 'OpenAI’s Consistency Models Support Fast One-Step Generation for Diffusion Models',\n",
              " 'Ecosystem Graphs: The Social Footprint of Foundation Models',\n",
              " 'What is diffusion model in deep learning?',\n",
              " 'Oxford University Researchers Introduce A Diffusion Model Called RealFusion That Can Generate 360-degree Reconstructions Of Objects From An Image',\n",
              " 'Alibaba AI Research Proposes Composer: A Large (5 Billion Parameters) Controllable Diffusion Model Trained on Billions of (Text, Image) Pairs']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "title_list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_YXBznt2_Ikw"
      },
      "source": [
        "(2) Organize sentiment score from Afinn, Word2Vec, DistilBert and save as second column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ceJpNXAs-N_y"
      },
      "outputs": [],
      "source": [
        "# Get sentiment analysis of each title by using AFINN\n",
        "from afinn import Afinn\n",
        "afinn = Afinn()\n",
        "afinn_score_list = [afinn.score(title) for title in title_list]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "93YifquF9G4Z"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "from transformers import DistilBertTokenizer, DistilBertForSequenceClassification"
      ]
    },
    {
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            "text/plain": [
              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
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            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/485 [00:00<?, ?B/s]"
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              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ]
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          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Get only the English sentiment analysis from Hugging Face's model hub\n",
        "pre_model = \"textattack/distilbert-base-uncased-imdb\"\n",
        "# Using DistilBert model\n",
        "tokenizer = DistilBertTokenizer.from_pretrained(pre_model)\n",
        "model = DistilBertForSequenceClassification.from_pretrained(pre_model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "hPJfuCfN9fGI"
      },
      "outputs": [],
      "source": [
        "def calc_sentiment_score(title):\n",
        "    # Tokenize the title and convert to tensors\n",
        "    inputs = tokenizer(title, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"]\n",
        "    attention_mask = inputs[\"attention_mask\"]\n",
        "\n",
        "    # Pass the input through the DistilBert model\n",
        "    with torch.no_grad():\n",
        "        outputs = model(input_ids, attention_mask=attention_mask)\n",
        "        logits = outputs.logits\n",
        "\n",
        "    # Calculate the sentiment score\n",
        "    sentiment_score = torch.softmax(logits, dim=1).numpy()[0]\n",
        "\n",
        "    return sentiment_score\n",
        "\n",
        "# Calculate sentiment scores for each title\n",
        "DistilBert_senti = []\n",
        "for title in title_list:\n",
        "    sentiment_score = calc_sentiment_score(title)\n",
        "    DistilBert_senti.append(\"Nagetive\" if sentiment_score[0] > sentiment_score[1] else \"Postive\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "goBlbtGU9uYG",
        "outputId": "52b6ba40-3a61-4212-d181-92afe44c5b09"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Nagetive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Postive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Nagetive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive',\n",
              " 'Postive']"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "DistilBert_senti"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "ttcmVTSMBtGH"
      },
      "outputs": [],
      "source": [
        "# Combine affin score and DistilBert_senti as 1 column\n",
        "combined_list = [f\"{afinn_score}/{DistilBert}\" for afinn_score, DistilBert in zip(afinn_score_list, DistilBert_senti)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0K5VIETBtMs",
        "outputId": "0bc77175-2e3a-4d3b-8dbf-cae8b48d9d94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['-1.0/Nagetive',\n",
              " '-2.0/Nagetive',\n",
              " '2.0/Nagetive',\n",
              " '0.0/Nagetive',\n",
              " '2.0/Nagetive',\n",
              " '1.0/Postive',\n",
              " '-1.0/Nagetive',\n",
              " '-2.0/Nagetive',\n",
              " '0.0/Nagetive',\n",
              " '4.0/Nagetive',\n",
              " '-2.0/Nagetive',\n",
              " '4.0/Postive',\n",
              " '1.0/Postive',\n",
              " '0.0/Postive',\n",
              " '0.0/Postive',\n",
              " '2.0/Postive',\n",
              " '0.0/Postive',\n",
              " '0.0/Postive',\n",
              " '0.0/Postive',\n",
              " '0.0/Postive']"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "combined_list"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "31AYgVioBaJ9"
      },
      "source": [
        "(3) Combine Column1 & Column2, Export into Excel file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "ThKScmGIBYzp"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "df = pd.DataFrame({\"News Title\": title_list, \"Sentiment Score(Affin/DistilBert)\": combined_list})\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "u6uyMLPABQhK",
        "outputId": "7b415632-e441-4b6e-a9e3-4f186c61f9e9"
      },
      "outputs": [
        {
          "data": {
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          "data": {
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              "<IPython.core.display.Javascript object>"
            ]
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          "metadata": {},
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        }
      ],
      "source": [
        "from google.colab import files\n",
        "file_name = \"Sentiment.xls\"\n",
        "df.to_excel(file_name, engine=\"openpyxl\")\n",
        "files.download(file_name)"
      ]
    },
    {
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      "execution_count": null,
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