1_P2_Semantic_Similarity_Sample.ipynb

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        "# Methodology of Scoring Semantic Similarity\n",
        "\n",
        "* In this section, we outline the methodology for scoring reviews based on their semantic similarity to our predefined list of emotions. As a preliminary trial, we have selected 27 reviews from two different beers featured on a single website."
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              "              beer_name  beer_id               brewery_name  brewery_id  \\\n",
              "0  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "1  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "2  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "3  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "4  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "\n",
              "            style  abv        date      user_name  user_id  appearance  aroma  \\\n",
              "0  Imperial Stout  8.0  1500976800        mR_fr0g   108099           4      7   \n",
              "1  Imperial Stout  8.0  1499076000         anstei   288109           2      4   \n",
              "2  Imperial Stout  8.0  1485255600  Beersiveknown   128086           4      8   \n",
              "3  Imperial Stout  8.0  1473847200     The_Osprey   249130           4      8   \n",
              "4  Imperial Stout  8.0  1471773600           rlgk     7982           4      7   \n",
              "\n",
              "   palate  taste  overall  rating  \\\n",
              "0       4      7       15     3.7   \n",
              "1       2      4        4     1.6   \n",
              "2       4      8       15     3.9   \n",
              "3       4      7       16     3.9   \n",
              "4       4      7       14     3.6   \n",
              "\n",
              "                                                text  \n",
              "0  Bottle shared at Chris & Ruthâs pre GBBF Shi...  \n",
              "1  Bottle at MC Zurich. Pours black with a medium...  \n",
              "2  Bottle shared with chriso et all and also a bo...  \n",
              "3  August 2016 - Bottle share at Chrisoâs Sunda...  \n",
              "4  Chris and Ruthâs Shindig 2016, Sunday. Black...  "
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              "      <th></th>\n",
              "      <th>beer_name</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "file_path = 'sample.xls'\n",
        "df = pd.read_excel(file_path)\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install sentence-transformers"
      ],
      "metadata": {
        "id": "wYeIZYtfdlnF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "model = SentenceTransformer('all-MiniLM-L6-v2')\n",
        "\n",
        "reviews = df['text'].tolist()\n",
        "\n",
        "joy = ['joy']\n",
        "sadness = ['sadness']\n",
        "anger = ['anger']\n",
        "fear = ['fear']\n",
        "love = ['love']\n",
        "surprise = ['surprise']"
      ],
      "metadata": {
        "id": "mPYQCZK6dlnG"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Computing embedding for both lists\n",
        "embeddings1 = model.encode(reviews, convert_to_tensor=True)\n",
        "embeddings2 = model.encode(joy, convert_to_tensor=True)\n",
        "embeddings3 = model.encode(sadness, convert_to_tensor=True)\n",
        "embeddings4 = model.encode(anger, convert_to_tensor=True)\n",
        "embeddings5 = model.encode(fear, convert_to_tensor=True)\n",
        "embeddings6 = model.encode(love, convert_to_tensor=True)\n",
        "embeddings7 = model.encode(surprise, convert_to_tensor=True)\n",
        "\n",
        "#Computing cosine-similarities\n",
        "joy_scores = util.cos_sim(embeddings1, embeddings2)\n",
        "sadness_scores = util.cos_sim(embeddings1, embeddings3)\n",
        "anger_scores = util.cos_sim(embeddings1, embeddings4)\n",
        "fear_scores = util.cos_sim(embeddings1, embeddings5)\n",
        "love_scores = util.cos_sim(embeddings1, embeddings6)\n",
        "surprise_scores = util.cos_sim(embeddings1, embeddings7)"
      ],
      "metadata": {
        "id": "sbfDXUl1dlnH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding the similarity scores as new columns to the DataFrame\n",
        "df['joy_score'] = joy_scores.tolist()\n",
        "df['sadness_score'] = sadness_scores.tolist()\n",
        "df['anger_score'] = anger_scores.tolist()\n",
        "df['fear_score'] = fear_scores.tolist()\n",
        "df['love_score'] = love_scores.tolist()\n",
        "df['surprise_score'] = surprise_scores.tolist()\n",
        "\n",
        "# Saving the updated DataFrame to a new CSV file\n",
        "df.to_csv('updated_data.csv', index=False)"
      ],
      "metadata": {
        "id": "If67ilqAf7c_"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 654
        },
        "id": "rBPAChWzgbw2",
        "outputId": "2b736b2f-9b64-430c-a605-26e2c88ca7f6"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              beer_name  beer_id               brewery_name  brewery_id  \\\n",
              "0  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "1  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "2  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "3  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "4  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "\n",
              "            style  abv        date      user_name  user_id  appearance  ...  \\\n",
              "0  Imperial Stout  8.0  1500976800        mR_fr0g   108099           4  ...   \n",
              "1  Imperial Stout  8.0  1499076000         anstei   288109           2  ...   \n",
              "2  Imperial Stout  8.0  1485255600  Beersiveknown   128086           4  ...   \n",
              "3  Imperial Stout  8.0  1473847200     The_Osprey   249130           4  ...   \n",
              "4  Imperial Stout  8.0  1471773600           rlgk     7982           4  ...   \n",
              "\n",
              "   taste  overall  rating                                               text  \\\n",
              "0      7       15     3.7  Bottle shared at Chris & Ruthâs pre GBBF Shi...   \n",
              "1      4        4     1.6  Bottle at MC Zurich. Pours black with a medium...   \n",
              "2      8       15     3.9  Bottle shared with chriso et all and also a bo...   \n",
              "3      7       16     3.9  August 2016 - Bottle share at Chrisoâs Sunda...   \n",
              "4      7       14     3.6  Chris and Ruthâs Shindig 2016, Sunday. Black...   \n",
              "\n",
              "               joy_score           sadness_score            anger_score  \\\n",
              "0  [0.13793334364891052]   [0.09664055705070496]   [0.1604452133178711]   \n",
              "1  [0.07160473614931107]  [0.055134691298007965]  [0.05836186930537224]   \n",
              "2  [0.04942207410931587]  [0.006872272118926048]  [0.03351932018995285]   \n",
              "3   [0.1778445839881897]   [0.06544509530067444]  [0.14239011704921722]   \n",
              "4  [0.16992557048797607]   [0.09210918843746185]  [0.14046210050582886]   \n",
              "\n",
              "                 fear_score             love_score         surprise_score  \n",
              "0     [0.07894943654537201]  [0.21690380573272705]  [0.11983683705329895]  \n",
              "1     [0.06901727616786957]  [0.08915862441062927]  [0.11216261237859726]  \n",
              "2  [-0.0033654700964689255]   [0.1062934622168541]  [0.07568646222352982]  \n",
              "3     [0.10937458276748657]    [0.271146297454834]  [0.13629484176635742]  \n",
              "4     [0.06581304967403412]  [0.20374217629432678]   [0.1373400241136551]  \n",
              "\n",
              "[5 rows x 22 columns]"
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              "      <th>1</th>\n",
              "      <td>Bullhouse El Capitan</td>\n",
              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
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              "      <td>[0.06901727616786957]</td>\n",
              "      <td>[0.08915862441062927]</td>\n",
              "      <td>[0.11216261237859726]</td>\n",
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              "      <td>8</td>\n",
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              "      <td>[0.07568646222352982]</td>\n",
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              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
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              "      <td>The_Osprey</td>\n",
              "      <td>249130</td>\n",
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              "      <td>[0.1778445839881897]</td>\n",
              "      <td>[0.06544509530067444]</td>\n",
              "      <td>[0.14239011704921722]</td>\n",
              "      <td>[0.10937458276748657]</td>\n",
              "      <td>[0.271146297454834]</td>\n",
              "      <td>[0.13629484176635742]</td>\n",
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              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
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              "      <td>rlgk</td>\n",
              "      <td>7982</td>\n",
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              "      <td>7</td>\n",
              "      <td>14</td>\n",
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              "      <td>[0.20374217629432678]</td>\n",
              "      <td>[0.1373400241136551]</td>\n",
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          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Preliminary Analysis for Model Selection**\n",
        "\n",
        "> In this part, our goal is to select the most effective multilingual model for our analysis, ensuring it can accurately differentiate between opposite emotions selected as joy and sadness.\n",
        "\n",
        "* We are trying to decide between 4 multilingual models listed below not to miss the reviews in different languages that exist in our dataset.\n",
        "\n",
        "* We will compare four different multilingual models to determine which one best represents the emotional spectrum by evaluating the score differences between emotions 'joy' and 'sadness'.\n",
        "\n"
      ],
      "metadata": {
        "id": "cROPYsgZjvHU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Multi-Lingual Models**\n",
        "\n",
        "> The models of sentence-transformers trained for many languages are listed below:\n",
        "\n",
        "**distiluse-base-multilingual-cased-v1**: Multilingual knowledge distilled version of multilingual Universal Sentence Encoder. Supports 15 languages: Arabic, Chinese, Dutch, English, French, German, Italian, Korean, Polish, Portuguese, Russian, Spanish, Turkish.\n",
        "\n",
        "**distiluse-base-multilingual-cased-v2**: Multilingual knowledge distilled version of multilingual Universal Sentence Encoder. This version supports 50+ languages, but performs a bit weaker than the v1 model.\n",
        "\n",
        "**paraphrase-multilingual-MiniLM-L12-v2** - Multilingual version of paraphrase-MiniLM-L12-v2, trained on parallel data for 50+ languages.\n",
        "\n",
        "**paraphrase-multilingual-mpnet-base-v2** - Multilingual version of paraphrase-mpnet-base-v2, trained on parallel data for 50+ languages.\n",
        "\n",
        "Reference: https://www.sbert.net/docs/pretrained_models.html"
      ],
      "metadata": {
        "id": "2b8-yRNpj1dl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "file_path = 'sample.xls'\n",
        "df2 = pd.read_excel(file_path)\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625
        },
        "id": "kefOIHsLrUTT",
        "outputId": "03dafaf9-af4c-4e1b-977c-bfa8766cfb6a"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              beer_name  beer_id               brewery_name  brewery_id  \\\n",
              "0  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "1  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "2  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "3  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "4  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "\n",
              "            style  abv        date      user_name  user_id  appearance  aroma  \\\n",
              "0  Imperial Stout  8.0  1500976800        mR_fr0g   108099           4      7   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sentence_transformers import SentenceTransformer, util\n",
        "model1 = SentenceTransformer('distiluse-base-multilingual-cased-v1')\n",
        "model2 = SentenceTransformer('distiluse-base-multilingual-cased-v2')\n",
        "model3 = SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2')\n",
        "model4 = SentenceTransformer('paraphrase-multilingual-mpnet-base-v2')\n",
        "\n",
        "reviews = df2['text'].tolist()\n",
        "\n",
        "joy = ['joy']\n",
        "sadness = ['sadness']"
      ],
      "metadata": {
        "id": "p39L8oGAo0gK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Computing embedding for both lists for all the models\n",
        "embeddings_1_a = model1.encode(reviews, convert_to_tensor=True)\n",
        "embeddings_1_b = model1.encode(joy, convert_to_tensor=True)\n",
        "embeddings_1_c = model1.encode(sadness, convert_to_tensor=True)\n",
        "\n",
        "embeddings_2_a = model2.encode(reviews, convert_to_tensor=True)\n",
        "embeddings_2_b = model2.encode(joy, convert_to_tensor=True)\n",
        "embeddings_2_c = model2.encode(sadness, convert_to_tensor=True)\n",
        "\n",
        "embeddings_3_a = model3.encode(reviews, convert_to_tensor=True)\n",
        "embeddings_3_b = model3.encode(joy, convert_to_tensor=True)\n",
        "embeddings_3_c = model3.encode(sadness, convert_to_tensor=True)\n",
        "\n",
        "embeddings_4_a = model4.encode(reviews, convert_to_tensor=True)\n",
        "embeddings_4_b = model4.encode(joy, convert_to_tensor=True)\n",
        "embeddings_4_c = model4.encode(sadness, convert_to_tensor=True)"
      ],
      "metadata": {
        "id": "z2ZOq-ero0gx"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Computing cosine-similarities for all the models\n",
        "joy_scores1 = util.cos_sim(embeddings_1_a, embeddings_1_b)\n",
        "sadness_scores1 = util.cos_sim(embeddings_1_a, embeddings_1_c)\n",
        "\n",
        "joy_scores2 = util.cos_sim(embeddings_2_a, embeddings_2_b)\n",
        "sadness_scores2 = util.cos_sim(embeddings_2_a, embeddings_2_c)\n",
        "\n",
        "joy_scores3 = util.cos_sim(embeddings_3_a, embeddings_3_b)\n",
        "sadness_scores3 = util.cos_sim(embeddings_3_a, embeddings_3_c)\n",
        "\n",
        "joy_scores4 = util.cos_sim(embeddings_4_a, embeddings_4_b)\n",
        "sadness_scores4 = util.cos_sim(embeddings_4_a, embeddings_4_c)"
      ],
      "metadata": {
        "id": "rbiWR0Y_rxt_"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the difference between joy and sadness scores for all the models\n",
        "df2['score_diff_1'] = joy_scores1 - sadness_scores1\n",
        "df2['score_diff_2'] = joy_scores2 - sadness_scores2\n",
        "df2['score_diff_3'] = joy_scores3 - sadness_scores3\n",
        "df2['score_diff_4'] = joy_scores4 - sadness_scores4\n",
        "\n",
        "# Saving the updated DataFrame to a new CSV file\n",
        "df2.to_csv('updated_data2.csv', index=False)\n"
      ],
      "metadata": {
        "id": "kdz7RFG9sanm"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating the absolute differences between joy and sadness scores for all the models\n",
        "df2['joy_sadness_score_abs_diff_1'] = abs(joy_scores1 - sadness_scores1)\n",
        "df2['joy_sadness_score_abs_diff_2'] = abs(joy_scores2 - sadness_scores2)\n",
        "df2['joy_sadness_score_abs_diff_3'] = abs(joy_scores3 - sadness_scores3)\n",
        "df2['joy_sadness_score_abs_diff_4'] = abs(joy_scores4 - sadness_scores4)\n",
        "\n",
        "# Calculating the average of the absolute differences for each model\n",
        "avg_abs_diff_1 = df2['joy_sadness_score_abs_diff_1'].mean()\n",
        "avg_abs_diff_2 = df2['joy_sadness_score_abs_diff_2'].mean()\n",
        "avg_abs_diff_3 = df2['joy_sadness_score_abs_diff_3'].mean()\n",
        "avg_abs_diff_4 = df2['joy_sadness_score_abs_diff_4'].mean()\n",
        "\n",
        "# Print the average absolute differences for each model\n",
        "print(\"Average Absolute Difference Model 1:\", avg_abs_diff_1)\n",
        "print(\"Average Absolute Difference Model 2:\", avg_abs_diff_2)\n",
        "print(\"Average Absolute Difference Model 3:\", avg_abs_diff_3)\n",
        "print(\"Average Absolute Difference Model 4:\", avg_abs_diff_4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7eatSiEo0gy",
        "outputId": "de5da9f8-90bc-4f57-c1e0-d8ac0294fefd"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Absolute Difference Model 1: 0.034360938\n",
            "Average Absolute Difference Model 2: 0.046473566\n",
            "Average Absolute Difference Model 3: 0.12992051\n",
            "Average Absolute Difference Model 4: 0.0681108\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> In this case, the model that best shows the difference between joy and sadness is model 3 which is *paraphrase-multilingual-MiniLM-L12-v2*"
      ],
      "metadata": {
        "id": "7NOOXUoCtcGA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
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          "height": 654
        },
        "id": "gC3lZiq2snDJ",
        "outputId": "8be1782e-6130-496a-807d-c9f14ed93c39"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              beer_name  beer_id               brewery_name  brewery_id  \\\n",
              "0  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "1  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "2  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "3  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "4  Bullhouse El Capitan   441604  Bullhouse Brewing Company       27006   \n",
              "\n",
              "            style  abv        date      user_name  user_id  appearance  ...  \\\n",
              "0  Imperial Stout  8.0  1500976800        mR_fr0g   108099           4  ...   \n",
              "1  Imperial Stout  8.0  1499076000         anstei   288109           2  ...   \n",
              "2  Imperial Stout  8.0  1485255600  Beersiveknown   128086           4  ...   \n",
              "3  Imperial Stout  8.0  1473847200     The_Osprey   249130           4  ...   \n",
              "4  Imperial Stout  8.0  1471773600           rlgk     7982           4  ...   \n",
              "\n",
              "   rating                                               text  score_diff_1  \\\n",
              "0     3.7  Bottle shared at Chris & Ruthâs pre GBBF Shi...      0.003929   \n",
              "1     1.6  Bottle at MC Zurich. Pours black with a medium...     -0.053292   \n",
              "2     3.9  Bottle shared with chriso et all and also a bo...      0.035151   \n",
              "3     3.9  August 2016 - Bottle share at Chrisoâs Sunda...      0.033852   \n",
              "4     3.6  Chris and Ruthâs Shindig 2016, Sunday. Black...      0.035561   \n",
              "\n",
              "   score_diff_2  score_diff_3 score_diff_4  joy_sadness_score_abs_diff_1  \\\n",
              "0      0.047032      0.159737     0.071288                      0.003929   \n",
              "1     -0.030284      0.030511    -0.049630                      0.053292   \n",
              "2      0.058740      0.096382     0.070102                      0.035151   \n",
              "3      0.045596      0.194106     0.085266                      0.033852   \n",
              "4      0.086692      0.169923     0.137073                      0.035561   \n",
              "\n",
              "   joy_sadness_score_abs_diff_2  joy_sadness_score_abs_diff_3  \\\n",
              "0                      0.047032                      0.159737   \n",
              "1                      0.030284                      0.030511   \n",
              "2                      0.058740                      0.096382   \n",
              "3                      0.045596                      0.194106   \n",
              "4                      0.086692                      0.169923   \n",
              "\n",
              "   joy_sadness_score_abs_diff_4  \n",
              "0                      0.071288  \n",
              "1                      0.049630  \n",
              "2                      0.070102  \n",
              "3                      0.085266  \n",
              "4                      0.137073  \n",
              "\n",
              "[5 rows x 24 columns]"
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              "      <th></th>\n",
              "      <th>beer_name</th>\n",
              "      <th>beer_id</th>\n",
              "      <th>brewery_name</th>\n",
              "      <th>brewery_id</th>\n",
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              "      <th>joy_sadness_score_abs_diff_3</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bullhouse El Capitan</td>\n",
              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
              "      <td>8.0</td>\n",
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              "      <td>108099</td>\n",
              "      <td>4</td>\n",
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              "      <td>3.7</td>\n",
              "      <td>Bottle shared at Chris &amp; Ruthâs pre GBBF Shi...</td>\n",
              "      <td>0.003929</td>\n",
              "      <td>0.047032</td>\n",
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              "      <td>0.071288</td>\n",
              "      <td>0.003929</td>\n",
              "      <td>0.047032</td>\n",
              "      <td>0.159737</td>\n",
              "      <td>0.071288</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bullhouse El Capitan</td>\n",
              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1499076000</td>\n",
              "      <td>anstei</td>\n",
              "      <td>288109</td>\n",
              "      <td>2</td>\n",
              "      <td>...</td>\n",
              "      <td>1.6</td>\n",
              "      <td>Bottle at MC Zurich. Pours black with a medium...</td>\n",
              "      <td>-0.053292</td>\n",
              "      <td>-0.030284</td>\n",
              "      <td>0.030511</td>\n",
              "      <td>-0.049630</td>\n",
              "      <td>0.053292</td>\n",
              "      <td>0.030284</td>\n",
              "      <td>0.030511</td>\n",
              "      <td>0.049630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bullhouse El Capitan</td>\n",
              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1485255600</td>\n",
              "      <td>Beersiveknown</td>\n",
              "      <td>128086</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>3.9</td>\n",
              "      <td>Bottle shared with chriso et all and also a bo...</td>\n",
              "      <td>0.035151</td>\n",
              "      <td>0.058740</td>\n",
              "      <td>0.096382</td>\n",
              "      <td>0.070102</td>\n",
              "      <td>0.035151</td>\n",
              "      <td>0.058740</td>\n",
              "      <td>0.096382</td>\n",
              "      <td>0.070102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bullhouse El Capitan</td>\n",
              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1473847200</td>\n",
              "      <td>The_Osprey</td>\n",
              "      <td>249130</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>3.9</td>\n",
              "      <td>August 2016 - Bottle share at Chrisoâs Sunda...</td>\n",
              "      <td>0.033852</td>\n",
              "      <td>0.045596</td>\n",
              "      <td>0.194106</td>\n",
              "      <td>0.085266</td>\n",
              "      <td>0.033852</td>\n",
              "      <td>0.045596</td>\n",
              "      <td>0.194106</td>\n",
              "      <td>0.085266</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bullhouse El Capitan</td>\n",
              "      <td>441604</td>\n",
              "      <td>Bullhouse Brewing Company</td>\n",
              "      <td>27006</td>\n",
              "      <td>Imperial Stout</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1471773600</td>\n",
              "      <td>rlgk</td>\n",
              "      <td>7982</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>3.6</td>\n",
              "      <td>Chris and Ruthâs Shindig 2016, Sunday. Black...</td>\n",
              "      <td>0.035561</td>\n",
              "      <td>0.086692</td>\n",
              "      <td>0.169923</td>\n",
              "      <td>0.137073</td>\n",
              "      <td>0.035561</td>\n",
              "      <td>0.086692</td>\n",
              "      <td>0.169923</td>\n",
              "      <td>0.137073</td>\n",
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              "<p>5 rows × 24 columns</p>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        const element = document.querySelector('#df-269563dd-8698-4e1a-90d6-a5271602589f');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
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              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-d160452a-f4cf-47a8-b346-e24993e56102 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    }
  ]
}