Update README.md

README.md

@@ -25,10 +25,107 @@ The dataset consists of the following columns:
 | `Label`     | Like count range category                                |
 | `Count`     | Number of tweets in the like count range category        |
 
+
+
+
+## How to Process the Data
+
+To process the dataset, you can use the following Python code. This code reads the CSV file, cleans the tweets, tokenizes and lemmatizes the text, and filters out non-English tweets.
+
+### Required Libraries
+
+Make sure you have the following libraries installed:
+
+```bash
+pip install pandas nltk langdetect
+```
+
+## Data Processing Code
+
+Here’s the code to process the tweets:
+
+```python
+import pandas as pd
+import re
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from langdetect import detect, LangDetectException
+
+# Define the TweetProcessor class
+class TweetProcessor:
+    def __init__(self, file_path):
+        """
+        Initialize the object with the path to the CSV file.
+        """
+        self.df = pd.read_csv(file_path)
+        # Convert 'text' column to string type
+        self.df['text'] = self.df['text'].astype(str)
+
+    def clean_tweet(self, tweet):
+        """
+        Clean a tweet by removing links, special characters, and extra spaces.
+        """
+        # Remove links
+        tweet = re.sub(r'https\S+', '', tweet, flags=re.MULTILINE)
+        # Remove special characters and numbers
+        tweet = re.sub(r'\W', ' ', tweet)
+        # Replace multiple spaces with a single space
+        tweet = re.sub(r'\s+', ' ', tweet)
+        # Remove leading and trailing spaces
+        tweet = tweet.strip()
+        return tweet
+
+    def tokenize_and_lemmatize(self, tweet):
+        """
+        Tokenize and lemmatize a tweet by converting to lowercase, removing stopwords, and lemmatizing.
+        """
+        # Tokenize the text
+        tokens = word_tokenize(tweet)
+        # Remove punctuation and numbers, and convert to lowercase
+        tokens = [word.lower() for word in tokens if word.isalpha()]
+        # Remove stopwords
+        stop_words = set(stopwords.words('english'))
+        tokens = [word for word in tokens if word not in stop_words]
+        # Lemmatize the tokens
+        lemmatizer = WordNetLemmatizer()
+        tokens = [lemmatizer.lemmatize(word) for word in tokens]
+        # Join tokens back into a single string
+        return ' '.join(tokens)
+
+    def process_tweets(self):
+        """
+        Apply cleaning and lemmatization functions to the tweets in the DataFrame.
+        """
+        def lang(x):
+            try:
+                return detect(x) == 'en'
+            except LangDetectException:
+                return False
+
+        # Filter tweets for English language
+        self.df = self.df[self.df['text'].apply(lang)]
+
+        # Apply cleaning function
+        self.df['cleaned_text'] = self.df['text'].apply(self.clean_tweet)
+        # Apply tokenization and lemmatization function
+        self.df['tokenized_and_lemmatized'] = self.df['cleaned_text'].apply(self.tokenize_and_lemmatize)
+
+```
+
+Feel free to add or modify any details according to your specific requirements!
+
+Let me know if there’s anything else you’d like to adjust or add!
+
+
+
+
+
+
 ## Usage
 
 This dataset can be used for various research purposes, including sentiment analysis, trend analysis, and event impact studies related to the Israel-Palestine conflict.
 For questions or feedback, please contact:
 
 - **Name:** Mehyar Mlaweh
-- **Email:** [email protected]
+- **Email:** [email protected]