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import pandas as pd
import requests
import io
from pymongo import MongoClient
# Function to connect to MongoDB.
def get_mongo_client():
client = MongoClient("mongodb+srv://groupA:[email protected]/?retryWrites=true&w=majority&appName=SentimentCluster")
db = client["sentiment_db"]
return db["tweets"]
# Function to insert data if the collection is empty.
def insert_data_if_empty():
collection = get_mongo_client()
if collection.count_documents({}) == 0:
print("🟢 No data found. Inserting dataset...")
csv_url = "https://huggingface.co/spaces/sharangrav24/SentimentAnalysis/resolve/main/sentiment140.csv"
try:
response = requests.get(csv_url)
response.raise_for_status()
df = pd.read_csv(io.StringIO(response.text), encoding="ISO-8859-1")
collection.insert_many(df.to_dict("records"))
print("✅ Data Inserted into MongoDB!")
except Exception as e:
print(f"❌ Error loading dataset: {e}")
# Function to get dataset summary from MongoDB.
def get_dataset_summary():
collection = get_mongo_client()
# Aggregate counts for each sentiment target.
pipeline = [
{"$group": {"_id": "$target", "count": {"$sum": 1}}}
]
results = list(collection.aggregate(pipeline))
# Map the sentiment target values to labels.
mapping = {"0": "Negative", "2": "Neutral", "4": "Positive"}
summary_parts = []
total = 0
for doc in results:
target = str(doc["_id"])
count = doc["count"]
total += count
label = mapping.get(target, target)
summary_parts.append(f"{label}: {count}")
summary = f"Total tweets: {total}. " + ", ".join(summary_parts) + "."
return summary
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