Set Up the Environment
- Ensure you have Python installed on your machine. Most systems come with Python pre-installed.
- Install necessary Python packages. Libraries like `requests` for API calls and `pandas` for data manipulation are crucial.
- Use the command line to install these packages with `pip`:
pip install requests pandas
Authenticate with Brandwatch API
- Brandwatch requires authentication via OAuth. You'll need your client ID and client secret.
- Perform OAuth authentication to retrieve an access token. Use the POST request to exchange credentials for a token:
import requests
def get_access_token(client_id, client_secret, url='https://api.brandwatch.com/oauth/token'):
data = {
'grant_type': 'client_credentials',
'client_id': client_id,
'client_secret': client_secret,
}
response = requests.post(url, data=data)
response.raise_for_status()
return response.json()['access_token']
Retrieve Data from Brandwatch
- Use the access token to make requests to the Brandwatch APIs.
- To fetch social media mentions, define a function that sends a GET request with the token:
def get_mentions(project_id, query_id, access_token, url='https://api.brandwatch.com/projects'):
headers = {
'Authorization': f'Bearer {access_token}'
}
response = requests.get(f'{url}/{project_id}/queries/{query_id}/data/mentions', headers=headers)
response.raise_for_status()
return response.json()
Process the Retrieved Data
- Convert the JSON data into a `pandas` DataFrame for easy manipulation and analysis.
- Inspect the DataFrame to understand the structure and available sentiment scores.
import pandas as pd
def process_mentions(mentions_data):
mentions = mentions_data['results']
df = pd.DataFrame(mentions)
return df
Analyze Sentiment
- Identify sentiment-related fields, such as positive, neutral, and negative mentions.
- Calculate sentiment scores or ratios to evaluate overall sentiment trends:
def analyze_sentiment(df):
sentiment_counts = df['sentiment'].value_counts(normalize=True) * 100
print("Sentiment Distribution (%):")
print(sentiment_counts)
return sentiment_counts
Visualize Sentiment Data
- Utilize visualization libraries such as `matplotlib` or `seaborn` to create visual representations of sentiment analysis.
- Develop charts to highlight positive, neutral, and negative sentiment proportions:
import matplotlib.pyplot as plt
def plot_sentiment(sentiment_counts):
sentiment_counts.plot(kind='bar', color=['green', 'grey', 'red'])
plt.title('Sentiment Analysis')
plt.xlabel('Sentiment')
plt.ylabel('Percentage')
plt.show()
Automate and Scale the Solution
- Encapsulate the process in functions or scripts to run regularly for updated sentiment insights.
- Consider using automation tools or scheduling scripts like `cron` jobs for periodic execution.
# Define main function to control flow execution
def main(client_id, client_secret, project_id, query_id):
token = get_access_token(client_id, client_secret)
mentions_data = get_mentions(project_id, query_id, token)
df = process_mentions(mentions_data)
sentiment_counts = analyze_sentiment(df)
plot_sentiment(sentiment_counts)
# Execute the script
# main('your_client_id', 'your_client_secret', 'your_project_id', 'your_query_id')
