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| title: ExcelWordCloud | |
| emoji: 🐨 | |
| colorFrom: blue | |
| colorTo: gray | |
| sdk: gradio | |
| sdk_version: 4.21.0 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| This Gradio app enables users to generate sentiment-based word clouds from text data in Excel files. It visually represents positive and negative sentiments using color-coded words, providing an intuitive analysis of textual data. Here's a brief guide on how to use it: | |
| ### How to Use | |
| 1. **Upload Excel File:** Click the "Upload Excel File" button to select and upload your Excel file. The file should contain textual data you wish to analyze. | |
| 2. **Enter Column Name:** Input the name of the column from your Excel file that contains the text data. This tells the app which column to use for generating the word cloud. | |
| 3. **Generate Word Cloud:** After uploading the file and specifying the column name, submit the form. The app will process the text data, identifying positive and negative words to generate a word cloud. Positive words are displayed in green, negative words in red, and neutral or unspecified words in gray. | |
| 4. **View Results:** The generated word cloud will be displayed as the output. This visual representation helps you quickly grasp the overall sentiment of the text data. | |
| ### Technical Details | |
| - The app uses the `opinion_lexicon` from NLTK to distinguish between positive and negative words. | |
| - A custom color function, `SimpleGroupedColorFunc`, assigns specific colors to words based on their sentiment. | |
| - The `wordcloud` library generates the visual representation, which is then recolored according to the sentiment analysis. | |
| - Gradio's interface (`gr.Interface`) creates a simple web app for interacting with the Python function, allowing for easy file upload and parameter input. | |
| ### Notes | |
| - Make sure the column name you enter matches exactly with one in the Excel file, including case sensitivity. | |
| - The word cloud provides a snapshot of the sentiment but does not convey the context or the intensity of sentiments. | |
| This tool is particularly useful for quickly analyzing customer feedback, reviews, or any textual data where understanding sentiment is valuable. | |