Text Sentiment Classification App for Media and Coursework

Sentiment analysis is often the first natural language task a developer meets, and for good reason: media teams gauge audience reaction, students analyze survey responses for projects, and businesses read the temperature of comments, all through the same underlying question of whether this text is positive, negative, or neutral, and how strongly. Modern language models perform this task remarkably well, and learning to harness them for classification rather than chat is a foundational skill.

The intern builds a sentiment analysis application in Python with a Streamlit interface. Users paste text, a review, a comment, a paragraph of feedback, or upload a small batch, and the app uses LangChain to run each item through a language model with prompts designed for classification: returning a sentiment label, a confidence indication, and a one-line justification citing the phrases that drove the call. The intern implements single and batch analysis modes, a results view that aggregates batch outcomes into simple counts and proportions, and prompt iterations that improve consistency on tricky inputs such as sarcasm and mixed sentiment.

The project teaches classification-oriented prompting, structured output handling, and honest evaluation, since the intern tests where the classifier fails, delivering a clean first NLP product and the judgment to know when its answers deserve trust.