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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.

License: MIT License

Python 0.02% Jupyter Notebook 99.97% HTML 0.01%
sentiment-analysis kwoc2021 kwoc nlp nlp-library nlu-engine nlu data-visualization plotly matplotlib-pyplot

public-sentiment-analysis-based-on-twitter-hashtags's People

Contributors

abhinay-beerukuri avatar aditimaurya avatar chayan-11 avatar chinmay-jain767 avatar cyber-machine avatar i-am-sayantan avatar jeevesh28 avatar peaceful-555 avatar preyam2002 avatar prrtk avatar rahulgupta9202 avatar rohitpadage avatar rudransh1084 avatar sash002 avatar sm745052 avatar smruti2002 avatar vishesh-soni avatar

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public-sentiment-analysis-based-on-twitter-hashtags's Issues

Make a small report of the Task 2 plot observations

๐Ÿ“ TASK 3:

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.

Make plots from the scores created from TASK 1

๐Ÿ“ TASK 2:

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.

Improve the Readme.md and data.md.

๐Ÿ“‘ Documentation

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.

Make sentiment scores from the tweets of the hashtags using dataset.csv

๐Ÿ“ TASK 1:

Make a jupyter notebook, in which you can write the codes to find the sentiment scores of the tweets from the dataset.csv.

  • About the task : For better understanding, please have a look here: TASK 1
  • contribution : For making a contribution please have look here: Contribute
  • Format : Format of the jupyter notebook should be this : only till part 1, and name should be Name_of_contributer.ipynb
  • Output : Here is a sample snippet of the output after adding the scores

Since there are multiple algorithms for making the scores, multiple contributors can participate in it and make their own notebook as a contribution.

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