Sentiment Analysis Excel Automation

I have a database containing 191 short statements that I need assessed purely on sentiment. Leveraging a solid, pre-trained sentiment model will be fastest for this job, so there’s no need to build or fine-tune anything from scratch—I simply want robust, reliable scores.

Here’s the flow I’m picturing. First, pull the statements from my source (CSV/SQL dump—whatever is easiest for you once we connect). Run each line through the chosen pre-trained model—VADER, TextBlob, or a Hugging Face transformer are all fine as long as the output includes a polarity score and a clear label (positive, neutral, negative). Finally, compile everything into a clean Excel file where each row contains: the original statement, the sentiment label, the numerical confidence/score, and any other metadata the model provides.

Python is my language of choice for the pipeline; pandas, openpyxl, and the relevant NLP library will keep things straightforward. If you can wrap the script in a Jupyter notebook or a single .py file that I can rerun later, even better.

Deliverable: one Excel spreadsheet with 191 fully evaluated statements plus the reusable script. Accuracy of the sentiment labels should align with the chosen model’s documented benchmarks, and the file must open without warnings in the latest desktop version of Excel.

If this sounds clear, let me know which model you’d like to apply and how quickly you can turn it around—I’m ready to get started immediately.