Update README.md

README.md

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 ---
 license: cc-by-nc-nd-4.0
+task_categories:
+- text-classification
+language:
+- en
+- fr
+- ja
+- it
+- de
+tags:
+- code
+- finance
 ---
+
+# Reviews on Messengers Dataset 🤳 ⭐️
+
+The Reviews on Messengers Dataset is a comprehensive collection of **200** the most recent customer reviews on **6** messengers obtained from the popular app store, **Google Play**. See the list of the apps below.
+This dataset encompasses reviews written in **5** different languages: English, French, German, Italian, Japanese.
+
+The dataset's multilingual nature makes it useful for natural language processing tasks, sentiment analysis algorithms, and other machine learning applications that require diverse language data for training and evaluation.
+
+The dataset can be highly valuable in training and fine-tuning machine learning models to automatically classify sentiments, predict customer satisfaction, or extract key information from customer reviews. 
+
+The data was scraped with `google-play-scraper` python lib by [TrainingData Team](https://trainingdata.pro/data-market?utm_source=kaggle&utm_medium=cpc&utm_campaign=6000-messengers-reviews-google-play).
+
+### Apps in the dataset and their IDs: 
+- Telegram: `'org.telegram.messenger'`,
+- Facebook Messenger: `'com.facebook.orca'`,
+- Whats App: `'com.whatsapp'`,
+- Viber: `'com.viber.voip'`,
+- Snapchat: `'com.snapchat.android'`,
+- We Chat: `'com.tencent.mm'`.
+
+### Languages in the dataset:
+- English: `EN`,
+- French: `FR`,
+- German: `DE`,
+- Italian : `IT`,
+- Japanese: `JP`
+
+# Content
+For each item, we extracted:
+- **reviewId**: ID of the review,
+- **userName**: name of the reviewer,
+- **userImage**: profile image of the reviewer,
+- **content**: text of the review,
+- **score**: number of stars given to the review,
+- **thumbsUpCount**: number of likes on the review,
+- **at**: date of the review,
+- **replyContent**: text of the developer's comment,
+- **repliedAt**: date of the developer's comment,
+- **appVersion**: version of the app,
+- **userLang**: language of the review,
+- **app_id**: ID of the app
+
+# Try to find the messenger with the most attentive support 😉
+
+## **[TrainingData](https://trainingdata.pro/data-market?utm_source=huggingface&utm_medium=cpc&utm_campaign=messengers-reviews-google-play)** provides high-quality data annotation tailored to your needs
+
+More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
+
+TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets**