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license: mit |
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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. |
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## Dataset Details |
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- **Date Range:** October 17, 2023 - December 17, 2023 |
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- **Total Tweets:** 15,478 |
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- **Unique Tweets:** 14,854 |
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## Data Description |
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The dataset consists of the following columns: |
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| Column | Description | |
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|------------|-----------------------------------------------------------| |
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| `id` | Unique identifier for the tweet | |
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| `link` | URL link to the tweet | |
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| `text` | Text content of the tweet | |
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| `date` | Date and time when the tweet was posted | |
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| `likes` | Number of likes the tweet received | |
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| `comments` | Number of comments the tweet received | |
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| `Label` | Like count range category | |
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| `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: |
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```bash |
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pip install pandas nltk langdetect |
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``` |
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## Data Processing Code |
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Here’s the code to process the tweets: |
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```python |
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import pandas as pd |
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import re |
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from nltk.tokenize import word_tokenize |
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from nltk.corpus import stopwords |
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from nltk.stem import WordNetLemmatizer |
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from langdetect import detect, LangDetectException |
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# Define the TweetProcessor class |
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class TweetProcessor: |
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def __init__(self, file_path): |
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""" |
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Initialize the object with the path to the CSV file. |
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""" |
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self.df = pd.read_csv(file_path) |
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# Convert 'text' column to string type |
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self.df['text'] = self.df['text'].astype(str) |
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def clean_tweet(self, tweet): |
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""" |
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Clean a tweet by removing links, special characters, and extra spaces. |
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""" |
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# Remove links |
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tweet = re.sub(r'https\S+', '', tweet, flags=re.MULTILINE) |
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# Remove special characters and numbers |
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tweet = re.sub(r'\W', ' ', tweet) |
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# Replace multiple spaces with a single space |
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tweet = re.sub(r'\s+', ' ', tweet) |
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# Remove leading and trailing spaces |
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tweet = tweet.strip() |
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return tweet |
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def tokenize_and_lemmatize(self, tweet): |
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""" |
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Tokenize and lemmatize a tweet by converting to lowercase, removing stopwords, and lemmatizing. |
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""" |
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# Tokenize the text |
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tokens = word_tokenize(tweet) |
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# Remove punctuation and numbers, and convert to lowercase |
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tokens = [word.lower() for word in tokens if word.isalpha()] |
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# Remove stopwords |
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stop_words = set(stopwords.words('english')) |
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tokens = [word for word in tokens if word not in stop_words] |
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# Lemmatize the tokens |
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lemmatizer = WordNetLemmatizer() |
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tokens = [lemmatizer.lemmatize(word) for word in tokens] |
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# Join tokens back into a single string |
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return ' '.join(tokens) |
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def process_tweets(self): |
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""" |
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Apply cleaning and lemmatization functions to the tweets in the DataFrame. |
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""" |
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def lang(x): |
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try: |
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return detect(x) == 'en' |
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except LangDetectException: |
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return False |
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# Filter tweets for English language |
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self.df = self.df[self.df['text'].apply(lang)] |
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# Apply cleaning function |
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self.df['cleaned_text'] = self.df['text'].apply(self.clean_tweet) |
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# Apply tokenization and lemmatization function |
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self.df['tokenized_and_lemmatized'] = self.df['cleaned_text'].apply(self.tokenize_and_lemmatize) |
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``` |
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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 |
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This dataset can be used for various research purposes, including sentiment analysis, trend analysis, and event impact studies related to the Israel-Palestine conflict. |
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For questions or feedback, please contact: |
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- **Name:** Mehyar Mlaweh |
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- **Email:** mehyarmlaweh0@gmail.com |
