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!

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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.

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Screenshots:

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Tech stack:

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

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Deployment:

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

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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.