- Streamlit
- Interface
- NLP
- Domain
- 2023
- Year
Overview
It is hard to tell what a pile of text is about at first glance. With open-ended survey answers, customer reviews or social media posts, the first need is not detailed analysis but a quick overview.
The application takes the text and cleans it first: punctuation, numbers and meaning-free connective words (stop words) are stripped. Word frequencies are then computed and a word cloud is generated at sizes proportional to those frequencies. The user can change the colour palette, maximum word count and shape mask to make the output presentation-ready.
Because it is built with Streamlit, users can run it in the browser with no installation. The tool provides a practical first step for seeing which themes dominate before moving into deeper text analysis.
Highlights
- Automatic text cleaning and stop word removal
- Word clouds sized by frequency
- Colour palette, word count and shape mask options
- A browser interface requiring no installation
Objectives
- Quickly surface the dominant themes in a body of text
- Produce presentation-ready visual output
- Open a door to text analysis for non-technical users
Solution
- A preprocessing pipeline: cleaning, normalisation, stop words
- Frequency computation and weighting
- A parametric visualization engine
- Instant preview with Streamlit
Approach
- 01Defining preprocessing rules
- 02Designing the visualization parameters
- 03Testing across different text types
Outcomes
- A body of text became interpretable in seconds
- Presentation-ready visuals were produced
- A fast discovery step before deeper analysis
Challenges
- The adequacy of the Turkish stop word list
- Merging word stems
- Keeping visual density from hurting readability
Selected Work
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