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Description
Description
it's crucial to perform in-depth data analysis and visualization to gain insights, discover patterns, and make informed decisions. This issue is focused on conducting an of the text data and creating visualizations that will aid our understanding.
Tasks
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Data Exploration:
- Perform initial data exploration to understand the structure and characteristics of the text dataset.
- Identify key statistics, such as word count distributions, text length, and unique tokens.
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Text Preprocessing:
- Clean and preprocess the text data, including tasks like lowercasing, punctuation removal, and stopword removal.
- Tokenize the text and create a vocabulary for further analysis.
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Descriptive Analysis:
- Calculate basic statistics, such as word frequency, to identify the most common terms in the dataset.
- Visualize the distribution of word frequencies using appropriate charts (e.g., word clouds, bar charts).
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Sentiment Analysis:
- Perform sentiment analysis to gauge the overall sentiment of the text data.
- Create sentiment score distributions and visualizations.
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Topic Modeling:
- Apply topic modeling techniques (e.g., LDA or NMF) to identify key topics within the text.
- Visualize topic distributions and their evolution over time (if applicable).
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Text Visualization:
- Create informative visualizations to present the results of the analysis, such as word clouds, scatter plots, or heatmaps.
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Insights and Findings:
- Summarize the key insights and findings derived from the data analysis and visualizations.
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Documentation:
- Update the project documentation with the analysis methodology and findings.
Acceptance Criteria:
- Submit a Jupyter notebook containing the tutorial and the necessary datasets if need
- Modify the README.md file to include the new tutorial and a link to the added notebook