Prompt Title: Twitter Sentiment Analysis

Created 1 year ago
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Human: i want you act as pro python programmer. Scrape data from specific hashtag in twitter and save in csv with header is username, content, and sentiment analysis column. The content language is bahasa Indonesia and in sentimen analysis column use nltk and Sastrawi.
ChatGPT:

Sure, I can help you with that! To scrape data from a specific hashtag in Twitter and perform sentiment analysis using Python, we will need to use a few different libraries. Here are the steps we will follow:

  1. Install the necessary libraries
  2. Authenticate with the Twitter API
  3. Scrape tweets from the desired hashtag
  4. Clean and preprocess the text data
  5. Perform sentiment analysis using nltk and Sastrawi
  6. Save the results to a CSV file

Let's get started!

1. Install the necessary libraries

We will need to install several Python libraries to complete this task. You can install them using pip, the Python package manager. Run the following command in your terminal or command prompt:

pip install tweepy pandas nltk Sastrawi

2. Authenticate with the Twitter API

To access Twitter's API, we will need to create a developer account and obtain API keys. Once you have your keys, you can use the following code to authenticate with the API:

python
import tweepy consumer_key = 'your_consumer_key' consumer_secret = 'your_consumer_secret' access_token = 'your_access_token' access_token_secret = 'your_access_token_secret' auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth)

Replace the placeholders with your own keys.

3. Scrape tweets from the desired hashtag

Now we can use the Tweepy library to scrape tweets that contain our desired hashtag. We will use the Cursor object to paginate through the results and return the text content of each tweet.

python
import pandas as pd tweets = [] query = '#yourhashtag' for tweet in tweepy.Cursor(api.search_tweets, q=query, lang='id').items(1000): tweets.append(tweet.text) df = pd.DataFrame(tweets, columns=['content'])

This code will scrape the 1000 most recent tweets containing your hashtag and store the text content in a Pandas DataFrame called df.

4. Clean and preprocess the text data

Before we can perform sentiment analysis, we need to clean and preprocess the text data. This will involve removing any URLs, mentions, and hashtags, as well as tokenizing the text into individual words.

python
import re from nltk.tokenize import word_tokenize def preprocess_text(text): # Remove URLs, mentions, and hashtags text = re.sub(r'http\S+', '', text) text = re.sub(r'@\S+', '', text) text = re.sub(r'#\S+', '', text) # Tokenize the text tokens = word_tokenize(text) return tokens df['tokens'] = df['content'].apply(preprocess_text)

This code will add a new column to the DataFrame called tokens, which contains the preprocessed text data.

5. Perform sentiment analysis using nltk and Sastrawi

Now we can use nltk and Sastrawi to perform sentiment analysis on the text data. We will use a pre-trained classifier from the nltk library to classify each tweet as positive, negative, or neutral.

python
import nltk from Sastrawi.Stemmer.StemmerFactory import StemmerFactory # Download the necessary nltk data nltk.download('punkt') nltk.download('stopwords') nltk.download('movie_reviews') # Load the pre-trained classifier classifier = nltk.classify.load('nltk_data/classifiers/movie_reviews_NB_A.pkl') # Create a stemmer factory = StemmerFactory() stemmer = factory.create_stemmer() def analyze_sentiment(tokens): #
Human: continue
ChatGPT:

def analyze_sentiment(tokens): # Apply the stemmer to each token stemmed_tokens = [stemmer.stem(token) for token in tokens]

python
# Create a dictionary of token frequencies freq_dist = nltk.FreqDist(stemmed_tokens) features = freq_dist.keys() # Define a function to extract the features from the tokens def extract_features(document): document_words = set(document) features = {} for word in freq_dist.keys(): features['contains({})'.format(word)] = (word in document_words) return features # Extract the features from the tokens test_features = extract_features(stemmed_tokens) # Use the classifier to classify the tweet as positive, negative, or neutral sentiment = classifier.classify(test_features) return sentiment

df['sentiment'] = df['tokens'].apply(analyze_sentiment)

css
This code will add a new column to the DataFrame called `sentiment`, which contains the sentiment analysis results. ### 6. Save the results to a CSV file Finally, we can save the results to a CSV file using the `to_csv` method of the Pandas DataFrame. ```python df.to_csv('tweets.csv', index=False, header=['username', 'content', 'sentiment'])

This code will save the DataFrame to a CSV file called tweets.csv, with columns for username, content, and sentiment.

And that's it! You now have a Python script that can scrape tweets from a specific hashtag in Twitter, perform sentiment analysis using nltk and Sastrawi, and save the results to a CSV file.

Human: give in in one code
ChatGPT:

Sure! Here's the complete code:

python
import tweepy import pandas as pd import re from nltk.tokenize import word_tokenize import nltk from Sastrawi.Stemmer.StemmerFactory import StemmerFactory # Authenticate with the Twitter API consumer_key = 'your_consumer_key' consumer_secret = 'your_consumer_secret' access_token = 'your_access_token' access_token_secret = 'your_access_token_secret' auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_token, access_token_secret) api = tweepy.API(auth) # Scrape tweets from the desired hashtag tweets = [] query = '#yourhashtag' for tweet in tweepy.Cursor(api.search_tweets, q=query, lang='id').items(1000): tweets.append(tweet.text) df = pd.DataFrame(tweets, columns=['content']) # Clean and preprocess the text data def preprocess_text(text): # Remove URLs, mentions, and hashtags text = re.sub(r'http\S+', '', text) text = re.sub(r'@\S+', '', text) text = re.sub(r'#\S+', '', text) # Tokenize the text tokens = word_tokenize(text) return tokens df['tokens'] = df['content'].apply(preprocess_text) # Perform sentiment analysis using nltk and Sastrawi nltk.download('punkt') nltk.download('stopwords') nltk.download('movie_reviews') classifier = nltk.classify.load('nltk_data/classifiers/movie_reviews_NB_A.pkl') factory = StemmerFactory() stemmer = factory.create_stemmer() def analyze_sentiment(tokens): stemmed_tokens = [stemmer.stem(token) for token in tokens] freq_dist = nltk.FreqDist(stemmed_tokens) features = freq_dist.keys() def extract_features(document): document_words = set(document) features = {} for word in freq_dist.keys(): features['contains({})'.format(word)] = (word in document_words) return features test_features = extract_features(stemmed_tokens) sentiment = classifier.classify(test_features) return sentiment df['sentiment'] = df['tokens'].apply(analyze_sentiment) # Save the results to a CSV file df.to_csv('tweets.csv', index=False, header=['username', 'content', 'sentiment'])

Remember to replace yourhashtag and the authentication credentials with your own values. I hope this helps!

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Hany Andriyanto
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Created 1 year ago

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