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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "7e59ad5c",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import random\n",
"\n",
"# Define the path to the full Yelp dataset file\n",
"full_data_path = \"yelp_academic_dataset_review.json\"\n",
"\n",
"# Define the path to save the sampled dataset file\n",
"sampled_data_path = \"yelp_academic_dataset_review_sampled.json\"\n",
"\n",
"# Define the number of reviews to sample (adjust as needed)\n",
"num_reviews_to_sample = 10000 # Example: Sample 10,000 reviews\n",
"\n",
"# Load all reviews from the full dataset\n",
"all_reviews = []\n",
"with open(full_data_path, \"r\", encoding=\"utf-8\") as f:\n",
" for line in f:\n",
" review = json.loads(line)\n",
" all_reviews.append(review)\n",
"\n",
"# Randomly sample a subset of reviews\n",
"sampled_reviews = random.sample(all_reviews, num_reviews_to_sample)\n",
"\n",
"# Save the sampled reviews to a new JSON file\n",
"with open(sampled_data_path, \"w\", encoding=\"utf-8\") as f:\n",
" for review in sampled_reviews:\n",
" json.dump(review, f)\n",
" f.write(\"\\n\")\n",
"\n",
"print(f\"Sampled {num_reviews_to_sample} reviews and saved to {sampled_data_path}\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f562ff04",
"metadata": {},
"outputs": [],
"source": [
"import gzip\n",
"\n",
"# Define the path to save the compressed dataset file\n",
"compressed_data_path = \"yelp_academic_dataset_review_sampled.json.gz\"\n",
"\n",
"# Compress the sampled dataset file using gzip\n",
"with open(sampled_data_path, \"rb\") as f_in:\n",
" with gzip.open(compressed_data_path, \"wb\") as f_out:\n",
" f_out.writelines(f_in)\n",
"\n",
"print(f\"Compressed file saved to {compressed_data_path}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "337f6649",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.metrics import classification_report, accuracy_score\n",
"\n",
"# Load the preprocessed Yelp dataset (sampled and compressed if applicable)\n",
"data_path = \"yelp_academic_dataset_review_sampled.json.gz\" # Adjust the path\n",
"data = pd.read_json(data_path, lines=True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e0936968",
"metadata": {},
"outputs": [
{
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" <th>review_id</th>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>This was such a trash experience. We signed up...</td>\n",
" <td>2021-07-29 16:10:10</td>\n",
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" <td>5</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>I have been going to Goshen Nail Salon for the...</td>\n",
" <td>2018-03-16 00:30:50</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>Ok. This place surprised me. I always thought ...</td>\n",
" <td>2018-06-01 23:56:44</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>Meets expectations, but quirky. The trucks re...</td>\n",
" <td>2016-06-29 15:57:34</td>\n",
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"text/plain": [
" review_id user_id business_id \\\n",
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"2 S1Lg07IGrupUDk7Uu9rnQQ umUy5DTpVrvQDXLR4gywHA H7BikysfQbS9bMULQsCU_Q \n",
"3 AH4_Pua0yzK4oU9FoU8hXQ uwYw0KKj16lC_nq_HsQGVQ Xb6QfBbleg2aJT2cG807jQ \n",
"4 9_CIDS98p6ZsTRiCvmuIKA l9bVKgzvjjcU8Iang3Tvtg lqSJkyNSE1yPeux4PoR-pg \n",
"... ... ... ... \n",
"9995 5MknizHCBH3jpj5DJd-6Uw d2VrfngFJ1f1nvNAsojJzw hy-E7DdXbdgTbwphKUYW1w \n",
"9996 mXFlaWuiCnyCkZ_SIAGqew cHWDGVf4LofBk9wZ2mnXQQ AYWSFv6QxF5IjQSxITMUug \n",
"9997 W1Ij-zC3ufRU5MTEgHLjmg aN9nWudz5rfar7rHr9lHfA oyJ3gXNkV0DO0YxcaTgtTg \n",
"9998 HNejB5H9iD1qe3MMKxg6sg 6JejVLZl5M-IB3UkNTkXtQ WJLKQTduGumxjlXelqiuKg \n",
"9999 LSJGzHJ7whqNn5uPxidMjQ _Av1LaAAY0Y8YcPp7Ck7fg M983OPfVRnwvG7zEOzykCA \n",
"\n",
" stars useful funny cool \\\n",
"0 4 1 0 1 \n",
"1 5 1 0 1 \n",
"2 2 4 1 0 \n",
"3 1 1 0 0 \n",
"4 1 0 0 0 \n",
"... ... ... ... ... \n",
"9995 1 1 0 0 \n",
"9996 5 0 0 0 \n",
"9997 5 0 0 0 \n",
"9998 3 0 0 0 \n",
"9999 5 0 0 0 \n",
"\n",
" text date \n",
"0 I had read about this place adding a second lo... 2011-02-08 17:48:40 \n",
"1 I had dinner at Tin Angel on Saturday and was ... 2012-04-16 13:30:02 \n",
"2 I was really excited to visit the store, havin... 2019-10-05 00:17:15 \n",
"3 I hired Two Men and a Truck for my recent move... 2016-06-02 13:27:24 \n",
"4 i was very disappointed to this company. They ... 2020-06-05 22:28:47 \n",
"... ... ... \n",
"9995 This was such a trash experience. We signed up... 2021-07-29 16:10:10 \n",
"9996 I have been going to Goshen Nail Salon for the... 2018-03-16 00:30:50 \n",
"9997 Ok. This place surprised me. I always thought ... 2018-06-01 23:56:44 \n",
"9998 Meets expectations, but quirky. The trucks re... 2016-06-29 15:57:34 \n",
"9999 Jordan was our waiter. He was very attentive a... 2017-03-15 23:54:07 \n",
"\n",
"[10000 rows x 9 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "466ef010",
"metadata": {},
"outputs": [],
"source": [
"# Map stars to sentiment labels\n",
"def map_sentiment(stars):\n",
" if stars >= 4:\n",
" return \"positive\"\n",
" elif stars <= 2:\n",
" return \"negative\"\n",
" else:\n",
" return \"neutral\" # Optional: Handle neutral sentiment if needed\n",
"\n",
"# Apply sentiment mapping to stars\n",
"data['sentiment'] = data['stars'].apply(map_sentiment)\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "756b3285",
"metadata": {},
"outputs": [],
"source": [
"# Apply sentiment mapping to stars\n",
"data['sentiment'] = data['stars'].apply(map_sentiment)\n",
"\n",
"# Split the data into training and testing sets\n",
"train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)\n",
"\n",
"# Save the preprocessed data\n",
"train_data.to_csv(\"preprocessed_train_data.csv\", index=False)\n",
"test_data.to_csv(\"preprocessed_test_data.csv\", index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7257dd9d",
"metadata": {},
"outputs": [],
"source": [
"pip install torch\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f03a2ad5",
"metadata": {},
"outputs": [],
"source": [
"pip install transformers"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ecdcf9c9",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import torch\n",
"from transformers import BertTokenizer, BertForSequenceClassification, Trainer, TrainingArguments\n",
"from sklearn.metrics import accuracy_score, precision_recall_fscore_support\n",
"\n",
"# Load the preprocessed training and testing data\n",
"train_data = pd.read_csv(\"preprocessed_train_data.csv\") # Adjust the path\n",
"test_data = pd.read_csv(\"preprocessed_test_data.csv\") # Adjust the path\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c83718d7",
"metadata": {},
"outputs": [],
"source": []
}
],
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