Update README.md

 # Facebook-Messenger-Bot
 # Facebook-Messenger-Bot
 
 
-The FB Messenger chatbot that I trained to talk like me. The associated [blog post](https://adeshpande3.github.io/adeshpande3.github.io/How-I-Used-Deep-Learning-to-Train-a-Chatbot-to-Talk-Like-Me).
+The FB Messenger chatbot that I trained to talk like me. 
+
+It refers to the contents of this blog [(Adit Deshpande-How I Used Deep Learning to train a chatbot talk to like me)](https://adeshpande3.github.io/adeshpande3.github.io/How-I-Used-Deep-Learning-to-Train-a-Chatbot-to-Talk-Like-Me),
+and author's git repository.
+
+This repository contains code for data preprocessing, seq2seq model, and wordToVec model. 
+
+In the case of the author, analyzed data such as facebook message in English, but I modified the code so that KakaoTalk data could be preprocessed, and some parameters were modified to properly analyze Korean data.
 
 
 ## Overview
 ## Overview
 
 
@@ -37,19 +44,9 @@ In order to run these scripts, you'll need the following libraries.
     ```
     ```
     This will give you all your Facebook conversations in a fairly unified text file. Thanks Dillon! Go ahead and then store that file in your Facebook-Messenger-Bot folder.
     This will give you all your Facebook conversations in a fairly unified text file. Thanks Dillon! Go ahead and then store that file in your Facebook-Messenger-Bot folder.
 
 
-* **LinkedIn Data**: Download your data from [here](https://www.linkedin.com/psettings/member-data). Once downloaded, you should see an **inbox.csv** file. We won't need to take any other steps here, we just want to copy it over to our folder.
-
-* **Google Hangouts Data**: Download your data form [here](https://takeout.google.com/settings/takeout/custom/chat). Once downloaded, you'll get a JSON file that we'll need to parse through. To do this, we'll use this [parser](https://takeout.google.com/settings/takeout/custom/chat) found through this phenomenal [blog post](https://blog.jay2k1.com/2014/11/10/how-to-export-and-backup-your-google-hangouts-chat-history/). We'll want to save the data into text files, and then copy the folder over to ours.
-
-    At the end of all this, you should have a directory structure that looks like this. Make sure you rename the folders and file names if yours are different.
-
-    ![](Images/DirectoryStructure.png)
-
-* **Discord Data** : You can extract your discord chatlogs by using this awesome [DiscordChatExporter](https://github.com/Tyrrrz/DiscordChatExporter) made by [Tyrrrz](https://github.com/Tyrrrz). Follow its documentation to extract your desired singular chat logs in `.txt` format (this is important). You can then put them all in a folder named `DiscordChatLogs` in the repo directory.
-
-* **WhatsApp Data**: Make sure you have a cell phone and put it in the US date-format if it is not already (this will be important later when you parse the log file to .csv). You can not use whatsApp web for this purpose. Open the chat you want to send, tap the menu button, tap more, then click "Email Chat". Send the email to yourself and download it to your computer. This will give you a .txt file, to parse it, we'll convert it to .csv. To do this go to [this link](http://ocpl.com.bd/whatsapp_parser/) and enter all the text in your log file. Click export, download the csv file and simply store it in your Facebook-Messenger-Bot folder under the name "whatsapp_chats.csv".
-
-  **NOTE**: The parser provided in the above link seems to have been removed. If you still have a `.csv` file *in the correct format*, you may still use that. Otherwise download your whatsapp chat logs as `.txt` files and put them all in a folder named `WhatsAppChatLogs` in the repo directory. `createDataset.py` will work with these files instead if, and only if, it **DOES NOT** find a `.csv` file named `whatsapp_chats.csv`
+* **KaKaotalk Data**: Tap the More section of the KakaoTalk window you want to analyze and enter the settings.
+* After that, you can send the KakaoTalk chat file to the designated email by clicking the Export chat details 
+* button, and enter all the text in your log file.
 
 
 4. Now that we have all our conversation logs in a clean format, we can go ahead and create our dataset. In our directory, let's run:
 4. Now that we have all our conversation logs in a clean format, we can go ahead and create our dataset. In our directory, let's run:
     ```bash
     ```bash