Understanding Social Analytics

1. Introduction

• Goal of the Tutorial:
  This tutorial aims to explain social analytics and its importance in formulating strategic decisions. You will learn how to gather and analyze data from various social media platforms.

• What You Will Learn:
  After completing this tutorial, you will understand the concept of social analytics, how to collect data from social media platforms, the methods of analyzing this data, and its role in strategic decision making.

• Prerequisites:
  Basic understanding of social media platforms and a knowledge of python programming would be helpful.

2. Step-by-Step Guide

• Social Analytics: Social Analytics is the process of gathering and analyzing data from social media platforms, which is then used to make strategic decisions. It can help in understanding the behavior and preferences of the target audience.

• Data Collection: The first step in social analytics is collecting the data. You can use APIs provided by the social media platforms to collect data.

• Data Analysis: After collecting the data, it needs to be analyzed. This can be done using various python libraries such as pandas and matplotlib.

• Strategic Decisions: The results of the data analysis can be used to make strategic decisions, such as identifying the best time to post or the type of content that gets the most engagement.

3. Code Examples

• Data Collection from Twitter:

# Importing required libraries
import tweepy

# Twitter API credentials
consumer_key = "YOUR_CONSUMER_KEY"
consumer_secret = "YOUR_CONSUMER_SECRET"
access_key = "YOUR_ACCESS_KEY"
access_secret = "YOUR_ACCESS_SECRET"

# Authenticating with Twitter
auth = tweepy.OAuthHandler(consumer_key, consumer_secret)
auth.set_access_token(access_key, access_secret)
api = tweepy.API(auth)

# Collecting tweets
public_tweets = api.home_timeline()
for tweet in public_tweets:
    print(tweet.text)

This code snippet collects the most recent tweets from your home timeline and prints them.

• Data Analysis:

# Importing required libraries
import pandas as pd

# Creating a dataframe with the tweets
df = pd.DataFrame([tweet.text for tweet in public_tweets], columns=['Tweets'])

# Displaying the first 5 rows of the dataframe
print(df.head())

This code snippet converts the collected tweets into a pandas dataframe and displays the first 5 rows.

4. Summary

In this tutorial, you learned about social analytics and its role in strategic decision making. You understood how to collect data from social media platforms and analyze it. The next step would be to learn how to visualize this data using libraries such as matplotlib or seaborn.

5. Practice Exercises

1. Collect tweets that mention a specific keyword and analyze the sentiment of these tweets.
2. Analyze the frequency of posting and engagement (likes, retweets) on your Twitter account.

Solutions:

1. For sentiment analysis, you can use libraries such as TextBlob. First, collect the tweets with the specific keyword and then analyze the sentiment of each tweet.
2. To analyze the frequency of posting, you can collect the timestamps of your tweets and plot them on a timeline. For engagement, you can collect the number of likes and retweets and analyze the relationship between the number of likes, retweets and the time of posting.

Further Practice:

Try to apply the same concepts to different social media platforms such as Facebook or Instagram.