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sentifluent 📉📊

A dashboard for viewing character targeted sentiment analysis and story stats.

It uses Selenium and BeautifulSoup for scraping comments + Pandas and NLTK for data preprocessing and sentiment analysis + Streamlit and Plotly Express for the dashboard and visulization. You can find the deployed website here!


Features:

  • The scrape.py files use Selenium and BeautifulSoup for scraping around 35k comments from over 100 chapters of my work on Wattpad, here.
  • The preprocess.py files clean, preprocess and split the comments into sentiment scores using NLTK's VADER lexicon. VADER relies on a dictionary that maps lexical features to emotion intensities known as sentiment scores.
  • Most computations are performed on the Compound sentiment score. The sentiment score of a text can be obtained by summing up the intensity of each word in the text.
  • The sentiment.py file creates the dashboard which visualises stats and inferences.

Screenshots:


Tech stack:

  • selenium and beautifulsoup: data mining.
  • pandas: formatting and cleaning the data.
  • nltk: sentiment analysis using VADER.
  • plotly express: visualisations.
  • streamlit: web framework.

Deployment:

The live project is deployed on https://share.streamlit.io/rubyruins/sentifluent/sentiment.py.


Local installation:

You must have Python 3.6 or higher to run the file.

  • Create a new virtual environment for running the application. You can follow the instructions here.
  • Navigate to the virtual environment and activate it.
  • Install the dependancies using pip install -r requirements.txt
  • Run the sentiment.py file with streamlit run sentiment.py

Note: to run the scraping and preprocessing scripts locally, you must have a version of chromedriver.exe that matches the one installed on your device.

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A dashboard for viewing character targeted sentiment analysis and story stats.

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