Boost Your Sentiment Analysis with Expert NLP Techniques

Boost Your Sentiment Analysis with Expert NLP TechniquesBoost Your Sentiment Analysis with Expert NLP Techniques

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Bulbul gupta

• Apr 2

I worked on a Sentiment Analysis project where I classified customer reviews into positive, negative, and neutral categories. I performed text preprocessing, used TF-IDF for feature extraction, and applied a machine learning model to improve accuracy. This project helped me gain hands-on experience in NLP, data labeling, and model evaluation.

AI Art Data Analysis Data Entry pandas Python scikit-learn

Istiak Ahmed Khan

• 12h

Great job

Bulbul gupta

• 4h

Thank you! 😊

Glad you liked it. I enjoy working on AI projects like sentiment analysis and always aim to deliver useful insights.

Happy to collaborate on similar work 🚀

Asad Aziz

• Apr 1

🚀 SMS Spam Detection Project

I’m excited to share one of my machine learning projects — an SMS Spam Detection System 📩

🔍 The goal of this project is to automatically classify incoming messages as spam or ham (legitimate messages).

💡 Key Features:

Machine Learning-based model

Text preprocessing & feature extraction

Accurate spam classification

Simple and user-friendly interface using Streamlit

🛠 Technologies Used:

Python

Scikit-learn

Pandas & NumPy

Streamlit

🌐 The project is available on GitHub and also deployed using Streamlit.

📎 Project Link: https://sms-spam-detection-001.streamlit.app/

🙏 Feel free to check it out and share your feedback. Your suggestions will help me improve further!

#MachineLearning #Python #AI #DataScience #Streamlit #GitHub #Learning

AI Development AI Engineer Python scikit-learn Streamlit Machine Learning Engineering

Marco Mastroeni

• Apr 1

Nice work! It seems really interesting. Some time ago i did something similar but for the news online. i sued textblob, nltk and vader for sentiment analysis. Did you use streamlit for the categorization of the words?

Vitor Teixeira

• Apr 1

A little sneak peek at what I've been working on...

AI Art Kling AI

Shania Mathews

• Mar 31

This project showcases my ability to transform messy, unstructured data into clean, organized, and actionable insights using Excel.

I worked with raw datasets containing inconsistencies such as duplicate entries, inconsistent formatting, and missing structure. I cleaned and standardized the data, ensuring accuracy and consistency across all fields.

From there, I built organized tables and dashboards to highlight key metrics such as revenue, profit, and performance by category and region. I also used formulas (including SUMIFS and calculated fields) to automate insights and improve usability.

This type of work is ideal for clients who need:

Clean and structured data

Reliable reporting

Easy-to-read dashboards

Improved data accuracy

The end result is a clear, professional spreadsheet that turns raw data into meaningful insights.

Data Entry Microsoft Excel Data Analysis

Geethasree Naguboina

pro

• Apr 3

The before and after view of the data cleaning really shows the value clearly — clients often don't realise how much time messy data costs them until they see it laid out like this. The monthly sales dashboard with KPIs on top is a clean layout too. Did you build this for a real client or as a portfolio piece?