Built a Sentiment Analysis Web App – My First Full-Stack ML Project
Elchin Nasirov developed a Sentiment Analysis Web App as his first full-stack machine learning project. The app predicts whether a given text is positive or negative using a Random Forest model. Nasirov shares insights on the challenges faced and the importance of a well-rounded approach to machine learning projects.
- ▪The web app features a React frontend and a Flask backend.
- ▪It uses a Random Forest model with TF-IDF for text processing.
- ▪Nasirov learned about the significance of model training data and deployment in machine learning.
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Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/nasirovelchin/built-a-sentiment-analysis-web-app-my-first-full-stack-ml-project-35f8 |
| Publication time | Wed, 27 May 2026 05:21:07 +0000 |
| Retrieval time | 2026-05-27T05:37:56.834Z |
| Last seen | 2026-05-27T05:37:56.834Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 4Vs2Mc8VAArN |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 362090) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Elchin Nasirov Posted on May 27 Built a Sentiment Analysis Web App – My First Full-Stack ML Project #machinelearning #webdev #programming #react Hey dev.to 👋 After spending a month learning Machine Learning through Andrew Ng’s specialization, I wanted to build something real — not just notebooks. So I created a Sentiment Analysis Web App — a full-stack project that takes any text and predicts whether it's Positive or Negative.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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