Sentiment Analysis for Customer Experience Reporting 📈📲🎵

The goal for this project was to apply advanced natural language processing techniques to analyze customer reviews and then, identify areas for improvement in the user experience.

"Fun Fact: This project idea popped out while I was reviewing LinkedIn Jobs. One of them roles got my attention, regarding customer experience improvement and teamwork, to test new features for an App."

1. Project description 👇

Development of KPI Dashboards and Reporting System, using Python, Pandas, and NLP tools.

For this project I used a "Spotify App Reviews" dataset from Kaggle. After being processed, the output was explored and saved in Tableau Public.

Problems detected to solve are as follows:

• Complex issues and gaps that affect customer experience
• There is no assigned role structure for the identified problems
• No reporting system to communicate issues effectively to stakeholders
• Insufficient capacity to reply customer/user reviews

2. Technology stack 💻

Programming language:

• Python

Python Libraries:

• matplotlib.pyplot: For data visualization.
• numpy: For mathematical operations and array manipulation.
• pandas: For data manipulation and analysis.
• nltk: For sentiment analysis among natural language processing tasks.

Functional architecture design:

3. Minimal Functional Product ⚙️

• Notebook with the code that processes the dataset, from its Data Cleaning to the Area Classification, described in the Functional architecture design.
• Dashboards for both Issues & Satisfaction Management uploaded to Tableau Public, containing the visualizations created from the processed dataset. The link is found in the Demo as main.txt file

Dashboards previews:

• Issues Management: Dashboard from Negative Reviews

• Satisfaction Management: Dashboard from Positive Reviews

  "I named this dashboard Spotiliebers (a parody term from Justin Bieber Fans) referring to Spotify Users Support, in order to give a fresh touch to this project".

4. Demo 📊📈📉

To show what it looks like in action, click here.

Note: The demo is also contained in the main.txt file from the Dashboard folder.

5. Folder structure 📁

└── project
    ├── _wip_
    ├── .gitignore
    ├── notebooks
    │   └── main.ipynb
    ├── images
    │   ├── pipeline.png
    │   ├── Issues_Dashboard.png
    │   └── Spotiliebers_Dashboard.png
    ├── data
    │   ├── raw
    │   │   └── spoti_reviews.csv
    │   └── processed
    │       └── reviews_report.csv
    ├── dashboard
    │   └── main.txt
    └── README.md    

6. Next steps 💡

• Develop an action plan to address problem areas identified in the 80-20
• Establish a multidisciplinary team with clearly defined roles to address the problem areas identified in the 80-20

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• Automate the connection with new data in the reporting system
• Integrate other data formats such as MySQL, PostgreSQL, JSON, XML

Contact info📧

For further information, reach me at andrew.bavuels@gmail.com