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google_search_api.py
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208 lines (185 loc) · 9.42 KB
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# -*- coding: utf-8 -*-
"""google search api.ipynb
Automatically generated by Colab.
Original file is located at
https://colab.research.google.com/drive/1Ke3AnXykN3lJS6hY19vsMVmCM0Ch67M9
"""
import requests
from textblob import TextBlob
import pandas as pd
# API Setup
#API_KEY = "" # API Key Hidden due to security reasons
CX = "71848bd9cccb64034"
# Search Function
def google_search(query, start_date, end_date):
results = []
url = "https://www.googleapis.com/customsearch/v1"
params = {
"key": API_KEY,
"cx": CX,
"q": query,
"dateRestrict": f"after:{start_date} before:{end_date}",
"num": 10
}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
for item in data.get("items", []):
snippet = item.get("snippet", "")
link = item.get("link", "")
results.append((snippet, link))
return results
# Sentiment Analysis Function
def analyze_sentiment(snippets):
sentiments = []
for snippet in snippets:
analysis = TextBlob(snippet)
sentiments.append(analysis.sentiment.polarity) # Polarity ranges from -1 to 1
return sum(sentiments) / len(sentiments) if sentiments else 0
# Designers and Timeframes
designers = [
{"name": "Burberry", "query": "Burberry", "start_date": "2022-01-01", "end_date": "2022-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2022-06-30", "end_date": "2022-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2023-01-01", "end_date": "2023-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2024-12-14", "end_date": "2024-06-30"},
{"name": "Riccardo Tisci Burberry", "query": "Riccardo Tisci Burberry", "start_date": "2018-01-01", "end_date": "2022-12-31"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2019-01-01", "end_date": "2023-12-31"},
{"name": "Kris Van Assche", "query": "Kris Van Assche Dior", "start_date": "2018-01-01", "end_date": "2019-06-30"}
]
# Fetch Data and Analyze Sentiment
results = []
for designer in designers:
search_results = google_search(designer["query"], designer["start_date"], designer["end_date"])
snippets = [result[0] for result in search_results]
sentiment_score = analyze_sentiment(snippets)
results.append({"Designer": designer["name"], "Sentiment Score": sentiment_score})
# Save Results
df = pd.DataFrame(results)
df.to_csv("designer_sentiment_scores.csv", index=False)
print(df)
import requests
from textblob import TextBlob
import pandas as pd
# API Setup
API_KEY = "AIzaSyCJjAdv4I-xApaFyh_JOn1nwa6aeJbxd3s" # Replace with your API Key
CX = "47424aed8ab33411a" # Replace with your Search Engine ID
# Search Function
def google_search(query, start_date, end_date):
results = []
url = "https://www.googleapis.com/customsearch/v1"
params = {
"key": API_KEY,
"cx": CX,
"q": query,
"dateRestrict": f"after:{start_date} before:{end_date}",
"num": 10
}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
for item in data.get("items", []):
snippet = item.get("snippet", "")
link = item.get("link", "")
results.append((snippet, link))
return results
# Sentiment Analysis Function
def analyze_sentiment(snippets):
sentiments = []
for snippet in snippets:
analysis = TextBlob(snippet)
sentiments.append(analysis.sentiment.polarity) # Polarity ranges from -1 to 1
return sum(sentiments) / len(sentiments) if sentiments else 0
# Designers and Timeframes
designers = [
{"name": "Daniel Lee Burberry", "query": "Daniel Lee", "start_date": "2022-01-01", "end_date": "2022-06-30"},
{"name": "Daniel Lee Burberry", "query": "Daniel Lee", "start_date": "2022-06-30", "end_date": "2022-12-31"},
{"name": "Daniel Lee Burberry", "query": "Daniel Lee", "start_date": "2023-01-01", "end_date": "2023-06-30"},
{"name": "Daniel Lee Burberry", "query": "Daniel Lee", "start_date": "2024-12-14", "end_date": "2024-06-30"},
{"name": "Riccardo Tisci Burberry", "query": "Burberry", "start_date": "2018-01-01", "end_date": "2022-12-31"},
{"name": "Kim Jones Dior", "query": "Dior", "start_date": "2019-01-01", "end_date": "2023-12-31"},
{"name": "Kris Van Assche", "query": "Kris Van Assche", "start_date": "2018-01-01", "end_date": "2019-06-30"}
]
# Fetch Data and Analyze Sentiment
results = []
for designer in designers:
search_results = google_search(designer["query"], designer["start_date"], designer["end_date"])
snippets = [result[0] for result in search_results]
sentiment_score = analyze_sentiment(snippets)
results.append({"Designer": designer["name"], "Sentiment Score": sentiment_score})
# Save Results
df = pd.DataFrame(results)
df.to_csv("designer_sentiment_scores.csv", index=False)
print(df)
import requests
from textblob import TextBlob
import pandas as pd
# API Setup
API_KEY = "your_google_api_key" # Replace with your API Key
CX = "your_search_engine_id" # Replace with your Search Engine ID
# Search Function
def google_search(query, start_date, end_date):
results = []
url = "https://www.googleapis.com/customsearch/v1"
params = {
"key": API_KEY,
"cx": CX,
"q": query,
"dateRestrict": f"after:{start_date} before:{end_date}",
"num": 10
}
response = requests.get(url, params=params)
if response.status_code == 200:
data = response.json()
for item in data.get("items", []):
snippet = item.get("snippet", "")
link = item.get("link", "")
results.append((snippet, link))
return results
# Sentiment Analysis Function
def analyze_sentiment(snippets):
sentiments = []
for snippet in snippets:
analysis = TextBlob(snippet)
sentiments.append(analysis.sentiment.polarity) # Polarity ranges from -1 to 1
return sum(sentiments) / len(sentiments) if sentiments else 0
# Designers and Timeframes
designers = [
{"name": "Burberry", "query": "Burberry", "start_date": "2022-01-01", "end_date": "2022-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2022-07-01", "end_date": "2022-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2023-01-01", "end_date": "2023-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2023-07-01", "end_date": "2023-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2018-01-01", "end_date": "2018-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2018-07-01", "end_date": "2018-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2019-01-01", "end_date": "2019-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2019-07-01", "end_date": "2019-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2020-01-01", "end_date": "2020-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2020-07-01", "end_date": "2020-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2021-01-01", "end_date": "2021-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2021-07-01", "end_date": "2021-12-31"},
{"name": "Burberry", "query": "Burberry", "start_date": "2022-01-01", "end_date": "2022-06-30"},
{"name": "Burberry", "query": "Burberry", "start_date": "2022-07-01", "end_date": "2022-12-31"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2019-01-01", "end_date": "2019-06-30"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2019-07-01", "end_date": "2019-12-31"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2020-01-01", "end_date": "2020-06-30"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2020-07-01", "end_date": "2020-12-31"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2021-01-01", "end_date": "2021-06-30"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2021-07-01", "end_date": "2021-12-31"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2022-01-01", "end_date": "2022-06-30"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2022-07-01", "end_date": "2022-12-31"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2023-01-01", "end_date": "2023-06-30"},
{"name": "Kim Jones Dior", "query": "Kim Jones Dior", "start_date": "2023-07-01", "end_date": "2023-12-31"},
{"name": "Kris Van Assche", "query": "Kris Van Assche Dior", "start_date": "2018-01-01", "end_date": "2018-06-30"},
{"name": "Kris Van Assche", "query": "Kris Van Assche Dior", "start_date": "2018-07-01", "end_date": "2018-12-31"},
{"name": "Kris Van Assche", "query": "Kris Van Assche Dior", "start_date": "2019-01-01", "end_date": "2019-06-30"}
]
# Fetch Data and Analyze Sentiment
results = []
for designer in designers:
search_results = google_search(designer["query"], designer["start_date"], designer["end_date"])
snippets = [result[0] for result in search_results]
sentiment_score = analyze_sentiment(snippets)
results.append({"Designer": designer["name"], "Start Date": designer["start_date"], "End Date": designer["end_date"], "Sentiment Score": sentiment_score})
# Save Results
df = pd.DataFrame(results)
df.to_csv("designer_sentiment_scores.csv", index=False)
print(df)