Update README.md

README.md

@@ -124,9 +124,9 @@ To identify the infomation embedded in the class comments across various project
 
 #### Initial Data Collection and Normalization
 
-It contains the dataset extracted from various projects of three programming languages Java, Smalltalk, and Python.
-- #### Java/ 
-     Contains all the extracted class comments of six java projects. 
+It contains the dataset extracted from various open-source projects of three programming languages Java, Smalltalk, and Python.
+- #### Java 
+     Each file contains all the extracted class comments from one project. We have a total of six java projects. We chose a sample of 350 comments from all these files for our experiment.
     - [Eclipse.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/) - Extracted class comments from the Eclipse project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Eclipse](https://github.com/eclipse).
     
     - [Guava.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Guava.csv) - Extracted class comments from the Guava project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Guava](https://github.com/google/guava).
@@ -142,7 +142,7 @@ It contains the dataset extracted from various projects of three programming lan
     - [Parser_Details.md](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Java/Parser_Details.md) - Details of the parser used to parse class comments of Java [ Projects](https://doi.org/10.5281/zenodo.4311839)
 
 - #### Smalltalk/
-     Contains all the extracted class comments of seven Pharo projects.     
+          Each file contains all the extracted class comments from one project. We have a total of seven Pharo projects. We chose a sample of 350 comments from all these files for our experiment.
     - [GToolkit.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/GToolkit.csv) - Extracted class comments from the GToolkit project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo.  
      
     - [Moose.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Moose.csv) - Extracted class comments from the Moose project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. 
@@ -160,7 +160,7 @@ It contains the dataset extracted from various projects of three programming lan
     - [Parser_Details.md](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Pharo/Parser_Details.md) - Details of the parser used to parse class comments of Pharo [ Projects](https://doi.org/10.5281/zenodo.4311839)
 
 - #### Python/
-     Contains all the extracted class comments of seven Python projects. 
+          Each file contains all the extracted class comments from one project. We have a total of seven Python projects. We chose a sample of 350 comments from all these files for our experiment.
     - [Django.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/Django.csv) -  Extracted class comments from the Django project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub [Django](https://github.com/django)
     
     - [IPython.csv](https://github.com/poojaruhal/RP-class-comment-classification/tree/main/Dataset/RQ1/Python/IPython.csv) -  Extracted class comments from the Ipython project. The version of the project referred to extract class comments is available as [Raw Dataset](https://doi.org/10.5281/zenodo.4311839) on Zenodo. More detail about the project is available on GitHub[IPython](https://github.com/ipython/ipython)