Sentiment Analysis Model Comparison with Machine Learning and GRU

Sentiment Analysis Model Comparison with Machine Learning and GRUSentiment Analysis Model Comparison with Machine Learning and GRU

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Aashutosh Kuikel

• Oct 17

Sentiment Analysis Using Machine Learning and Deep Learning

I developed a text-classification project to compare traditional machine learning algorithms with a deep learning model for sentiment analysis.

The project covers text preprocessing, feature extraction, model training, performance evaluation, and saving trained models for reuse.

What I Built

Text preprocessing: Prepared review text for model training.

Feature extraction: Used TF-IDF for traditional machine learning models and tokenized sequences for the GRU model.

Model comparison: Evaluated Logistic Regression, Multinomial Naive Bayes, Linear SVM, Random Forest, and GRU.

Performance evaluation: Generated classification reports and visualizations to compare model results.

Model persistence: Saved trained models and preprocessing components for reuse.

Tech Stack

Python · Pandas · NumPy · Scikit-learn · TensorFlow/Keras · NLP · TF-IDF · Matplotlib · Joblib

What This Project Demonstrates

This project gave me practical experience building and evaluating NLP pipelines, comparing different modelling approaches, and examining how model choice affects text-classification performance.

Source Code

View the project on GitHub

Completed work

AI Development Data Engineering Matplotlib Python Machine Learning Engineering TensorFlow

Denisa Georgiana Voicu

pro

• Oct 17

I created this font & it is free to download and use in your free or paid projects. Enjoy 🚀 find it at https://twaperdigital.com/anima-editorial

Completed work

Art Direction Graphic Design Typography ChatGPT FontForge Python

Mahmudul Hasan Manik

pro

• Oct 19

I like this font

Denisa Georgiana Voicu

pro

• Oct 17

Anima is a self-initiated type design project inspired by vintage print, worn ink and analog photography.

The collection includes two experimental serif typefaces: Anima Editorial, with elegant proportions and contrasting strokes, and Anima Retro, with softer, bolder shapes. Both feature grain built into the letterforms.

I developed the creative direction, custom fonts and promotional artwork, combining AI-assisted exploration with font development. The campaign pairs textured typography with motion-blurred photography to capture a warm, nostalgic mood. FREE DOWNLOAD AT https://twaperdigital.com/anima-retro

Completed work

Typography Art Direction Graphic Design ChatGPT FontLab Python

Mostafa Moamen

• Oct 17

Great job Denisa the font style background and it's effects are great 🎉🎉🎉🎉

keep going Denisa🔥🔥🔥🔥

Promise Esemuede

• Oct 17

CrowdVision AI – Real-Time Crowd Monitoring & Intelligence System

An AI-powered computer vision platform that detects and counts people in real time, monitors crowd density and occupancy, tracks entry and exit movements, and provides actionable crowd intelligence through a live monitoring dashboard.

Overview CrowdVision AI is an intelligent crowd monitoring and safety analytics platform designed to help organisations monitor public spaces, understand crowd behaviour, and identify potentially unsafe crowd conditions. The system uses computer vision and machine learning inference to analyse camera feeds and video streams, detect people, estimate occupancy, classify crowd density, and monitor movement across designated zones.

The Problem: Monitoring crowded environments manually can be difficult, time-consuming, and inefficient. Security personnel may struggle to maintain accurate occupancy counts, monitor multiple zones, and identify unusual crowd conditions quickly. CrowdVision AI was developed to explore how computer vision and real-time analytics could improve crowd visibility and monitoring.

My Solution: I developed a computer vision system integrating a pretrained YOLO object-detection model with OpenCV for video processing.

The platform includes: Real-time people detection and counting. Crowd occupancy estimation. Crowd-density classification. Entry and exit line counting. Zone-based monitoring. Incident and anomaly monitoring workflows. Camera health and tamper monitoring. Evidence management and reporting. Telegram alert notifications. Remote edge camera processing. Live monitoring dashboard.

Technical Implementation: Machine Learning & Computer Vision Python YOLO / Ultralytics OpenCV NumPy Pandas

Backend: Python FastAPI REST API

Frontend: React Vite JavaScript Other Technologies Git GitHub Telegram Bot API JSON/CSV data storage

My Role Founder & Full-Stack Machine Learning Developer

Completed work

AI Development OpenCV AI Engineer Web Development Python PyTorch

Kapil Chauhan

• Oct 19

For entry/exit line counting, I'd test someone pausing on the boundary and stepping back without entering. Do you require a track to move fully between two zones before recording an entry, so small position changes don't become extra visits?