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---
license: mit
---
This dataset contains tweets related to the Israel-Palestine conflict from October 17, 2023, to December 17, 2023. It includes information on tweet IDs, links, text, date, likes, and comments, categorized into different ranges of like counts.
## Dataset Details
- **Date Range:** October 17, 2023 - December 17, 2023
- **Total Tweets:** 15,478
- **Unique Tweets:** 14,854
## Data Description
The dataset consists of the following columns:
| Column | Description |
|------------|-----------------------------------------------------------|
| `id` | Unique identifier for the tweet |
| `link` | URL link to the tweet |
| `text` | Text content of the tweet |
| `date` | Date and time when the tweet was posted |
| `likes` | Number of likes the tweet received |
| `comments` | Number of comments the tweet received |
| `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:** mehyarmlaweh0@gmail.com |