This project focuses on sentiment analysis. Social Sentiment analysis is the use of natural language processing (NLP) to analyze social conversations online and determine deeper context as they apply to a topic, brand or theme.
After completing #4 you can make a report from the plots and complete task 3 of this project.
About the task: For better understanding, please have a look here: TASK 3
contribution : For making a contribution please have a look here: Contribute
Format: For this task you need to make a report, the format of the report should be this : and name should be Name_of_contributer.pdf.
Since a person can make different observations from plots and solve different unsolved questions, multiple contributors can participate in it and make their own notebook as a contribution.
After completing #3 you can make the plots from the scores and complete task 2 of this project. This task should be done in the same notebook in which you had done task 1.
About the task : For better understanding, please have a look here: TASK 2
contribution : For making a contribution please have look here: Contribute
Format : Format of the jupyter notebook should be this : part1 + part 2, and name should be Name_of_contributer.ipynb
Output: You can make line plots, bar plots, pie charts etc from the dataset.
Since a person can make multiple plots and make different observations from them, multiple contributors can participate in it and make their own notebook as a contribution.
If you are able to understand the tasks of the project, then you add some more parts in the Readme.md and in data.md
1. as you can add new parts in the readme, like research papers in this domain, kaggle solutions and Github links, and put the name as supporting material
2. You can make the abstract better.
3. Add more points to the data.md and describe more about the columns, as well more observational points.