Analyzing Elon Musk's Twitter Sentiment with Python

Table of Contents

1. Introduction
2. Setting up the Environment
3. Retrieving Tweets from Twitter API
4. Analyzing Sentiment with Open AI API
5. Visualizing Sentiment Analysis Results
6. Improving Sentiment Analysis Accuracy
7. Conclusion

Introduction

In this tutorial guide, we will explore the process of using the Twitter API and the Open AI API to Create a Python script that retrieves the latest tweets from Elon Musk on Twitter. We will then use the Open AI API to analyze the sentiment of these tweets, categorizing them as positive, negative, or neutral. Finally, we will create visualizations to showcase the sentiment trends of Elon Musk's tweets over time.

Setting up the Environment

Before we dive into the implementation, we need to set up the necessary environment to access the Twitter API and the Open AI API. We will start by creating a Twitter developer account and obtaining the required API keys. We will then create an Open AI account and retrieve the API key. Once we have all the necessary credentials, we can proceed to the next steps.

Creating a Twitter Developer Account

To access the Twitter API, we need to create a Twitter developer account. Follow these steps to create an account:

1. Visit the Twitter Developer Website (developer.twitter.com).
2. Sign up for a Twitter account if You don't already have one.
3. Add a phone number to your Twitter account for verification purposes.
4. Create a new project to obtain API keys.
5. Save the API keys for later use.

Creating an Open AI Account

To utilize the Open AI API, we need to have an Open AI account and obtain the API key. Follow these steps to set up your account:

1. Visit the Open AI Website (OpenAI.com) and sign up for an account if you don't already have one.
2. Access your account settings to retrieve the API key.
3. Save the API key for later use.

Now that we have our Twitter API keys and Open AI API key, We Are ready to proceed with the implementation.

Retrieving Tweets from Twitter API

In this section, we will write a function to retrieve the latest tweets from Elon Musk using the Twitter API. We will use the Tweepy library to Interact with the Twitter API conveniently. The function will take a keyword, date, and count as parameters, allowing us to customize the search criteria.

Importing Required Libraries

Before we start, let's import the necessary libraries: Tweepy for accessing the Twitter API and Pandas and Matplotlib for data manipulation and visualization.

import tweepy
import pandas as pd
import matplotlib.pyplot as plt

Authenticating with the Twitter API

To authenticate with the Twitter API, we need to use our Twitter API keys. Let's define a function to authenticate the user and create an instance of the Twitter API.

def authenticate_twitter_api():
    api_key = "YOUR_TWITTER_API_KEY"
    api_secret_key = "YOUR_TWITTER_API_SECRET_KEY"
    access_token = "YOUR_TWITTER_ACCESS_TOKEN"
    access_token_secret = "YOUR_TWITTER_ACCESS_TOKEN_SECRET"

    auth = tweepy.OAuthHandler(api_key, api_secret_key)
    auth.set_access_token(access_token, access_token_secret)

    api = tweepy.API(auth)
    return api

Make sure to replace the placeholders with your actual Twitter API keys.

Retrieving Tweets

Now, let's define the get_tweets function that takes a keyword, date, and count as parameters. This function will make use of the Twitter API to retrieve and store the tweets.

def get_tweets(keyword, date, count):
    api = authenticate_twitter_api()
    tweets = []

    # Retrieve tweets using the Twitter API
    for tweet in tweepy.Cursor(api.search, q=keyword, until=date, tweet_mode="extended").items(count):
        tweets.append(tweet.full_text)

    # Create a dataframe to store the retrieved tweets
    df = pd.DataFrame({'Tweets':tweets})

    return df

The function retrieves tweets containing the specified keyword until the specified date and limits the count to the specified number of tweets. The tweets are then stored in a Pandas dataframe for further analysis.

Call the get_tweets function with the desired parameters to retrieve the tweets.

tweets_df = get_tweets("Elon Musk", "2023-01-16", 5)
print(tweets_df)

The function will output the retrieved tweets in a readable format.

Analyzing Sentiment with Open AI API

In this section, we will analyze the sentiment of the retrieved tweets using the Open AI API. We will utilize the GPT-3 model from Open AI to classify the sentiment as positive, negative, or neutral.

Setting up Open AI API

To use the Open AI API, we need to import the openai library and set our Open AI API key.

import openai

openai.api_key = "YOUR_OPENAI_API_KEY"

Make sure to replace the placeholder with your actual Open AI API key.

Analyzing Sentiment

Let's define the analyze_sentiment function that takes a tweet as input and uses the Open AI API to classify the sentiment.

def analyze_sentiment(tweet):
    prompt = "Return the most likely sentiment opinion towards Elon Musk of the person who posted this tweet: positive, negative, or neutral.\n\nTweet: " + tweet

    response = openai.Completion.create(
        engine="text-davinci-003",
        prompt=prompt,
        max_tokens=10,
        temperature=0.1
    )

    sentiment = response.choices[0].text.strip()

    return sentiment

The function constructs a prompt by combining a predefined Context with the tweet. It then sends the prompt to the Open AI API and retrieves the sentiment classification. The sentiment is extracted from the API response and returned.

Call the analyze_sentiment function for each tweet in the dataframe and store the sentiment values in a new column.

tweets_df['Sentiment'] = tweets_df['Tweets'].Apply(analyze_sentiment)
print(tweets_df)

The function will output the dataframe with the added sentiment column.

Visualizing Sentiment Analysis Results

Now that we have the sentiment analysis results for the tweets, let's create visualizations to showcase the sentiment trends of Elon Musk's tweets over time.

Cleaning the Dataframe

Before generating visualizations, let's clean up the dataframe to have distinct positive, negative, and neutral values instead of text.

tweets_df['Sentiment'] = tweets_df['Sentiment'].replace({"positive": 1, "negative": -1, "neutral": 0})

Now, the sentiment column contains numerical values (-1, 0, 1) representing negative, neutral, and positive sentiment, respectively.

Grouping the Data by Date

To analyze sentiment trends over time, we need to group the tweets by date and calculate the average sentiment for each date.

grouped_df = df.groupby(pd.to_datetime(df['Date']).dt.date).mean()

The dataframe is grouped by converting the 'Date' column to a datetime object and extracting only the date part. The average sentiment is calculated for each date.

Plotting the Sentiment Trends

Finally, let's create a line plot to Visualize the sentiment trends over time.

plt.plot(grouped_df.index, grouped_df['Sentiment'])
plt.xlabel('Date')
plt.ylabel('Sentiment')
plt.title('Sentiment Analysis of Elon Musk Tweets')
plt.show()

The line plot displays the sentiment values along the y-axis and the corresponding dates along the x-axis. It provides an overview of the sentiment trends over the analyzed period.

Improving Sentiment Analysis Accuracy

While the sentiment analysis results give us a general understanding of the sentiment expressed in Elon Musk's tweets, there are certain limitations and opportunities for improvement. The Open AI API may not always accurately classify sentiment for certain tweets, leading to potential misclassifications.

To improve the accuracy of sentiment analysis, there are two main approaches:

1. Fine-tuning the model: Open AI provides options for fine-tuning their models. By fine-tuning the sentiment classification model on a dataset specific to Elon Musk's tweets, we can enhance the accuracy and relevance of the sentiment analysis results.

2. Increasing tweet volume: In this tutorial, we focused on retrieving a small number of tweets for demonstration purposes. However, by increasing the count of retrieved tweets and analyzing sentiment over a more extended period, we can obtain a more comprehensive understanding of sentiment trends.

By incorporating these improvements, we can achieve more accurate and Meaningful sentiment analysis results.

Conclusion

In this tutorial guide, we explored how to retrieve tweets from the Twitter API and analyze the sentiment of those tweets using the Open AI API. We visualized the sentiment trends of Elon Musk's tweets over time and discussed potential avenues for improving sentiment analysis accuracy.

While sentiment analysis provides valuable insights into the sentiment expressed in tweets, it is crucial to interpret the results cautiously, considering the limitations and opportunities for improvement.

Thank you for following along! Feel free to explore the provided code on GitHub and Continue expanding the project with fine-tuning and further analysis. Stay curious and keep learning!