| --- |
| license: mit |
| language: |
| - en |
| pretty_name: X |
| --- |
| |
| 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 |
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| - **Date Range:** October 17, 2023 - December 17, 2023 |
| - **Total Tweets:** 15,478 |
| - **Unique Tweets:** 14,854 |
|
|
| ## Data Description |
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| The dataset consists of the following columns: |
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| | 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 | |
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|
|
| ## How to Process the Data |
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|
| 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. |
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|
| ### Required Libraries |
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|
| Make sure you have the following libraries installed: |
|
|
| ```bash |
| pip install pandas nltk langdetect |
| ``` |
|
|
| ## Data Processing Code |
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|
| Here’s the code to process the tweets: |
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|
| ```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) |
| |
| ``` |
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| Feel free to add or modify any details according to your specific requirements! |
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| Let me know if there’s anything else you’d like to adjust or add! |
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|
|
| ## 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 |
