Beginner’s Guide to Building an Advanced Sentiment Analysis Dashboard with Streamlit and Ollama

Author(s): Anoop Maurya

Originally published on Towards AI.

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Sentiment analysis is a crucial tool in today’s data-driven world, enabling companies to understand customer feedback and monitor brand reputation. In this article, we’ll dive into building a sentiment analysis dashboard using TinyLlama and Streamlit, allowing you to analyze text sentiment with ease. I’ll walk you through the setup and implementation, and we’ll see some cool visualizations of the results.

The dashboard provides a user-friendly interface to analyze sentiment using different language models (LLMs). Here’s what you can do:

Model Selection: Choose from various LLMs installed on Ollama. For our demo, we’re using the lightweight TinyLlama model.Text Input: Enter the text you wish to analyze. Batch analysis is also supported for handling multiple texts at once.Real-time Results: The dashboard displays the sentiment (positive, negative, or neutral) along with confidence scores and word cloud visualizations.

Let’s take a closer look at the screenshots provided to see how it all comes together.

In the first screenshot, we see the initial user interface:

Settings Panel: Here, you can select the desired LLM model. The current selection is “TinyLlama,” which is known for its lightweight and efficient processing.Input Text Field: The dashboard provides a… Read the full blog for free on Medium.

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Published via Towards AI