You will get Sentiment Analysis Using Naive Bayes
Project details
implemented several machine learning algorithms like Naive Bayes, Maximum Entropy, Decision Tree, Random Forest, XGBoost, SVM, Multi-Layer Perceptron, Recurrent Neural networks and Convolutional Neural Networks to classify the polarity of the tweet. We used two types of features namely unigrams and bigrams for classification and observes that augmenting the feature vector with bigrams improved the accuracy. Once the feature has been extracted it was represented as either a sparse vector or a dense vector. It has been observed that presence in the sparse vector representation recorded a better performance than frequency.
Neural methods performed better than other classifiers in general. Our best LSTM model achieved an accuracy of 83.0% on Kaggle while the best CNN model achieved 83.34%. The model which used features from our best CNN model and classifies using SVM performed slightly better than only CNN. We finally used an ensemble method taking a majority vote over the predictions of 5 of our best models achieving an accuracy of 83.58%.
Neural methods performed better than other classifiers in general. Our best LSTM model achieved an accuracy of 83.0% on Kaggle while the best CNN model achieved 83.34%. The model which used features from our best CNN model and classifies using SVM performed slightly better than only CNN. We finally used an ensemble method taking a majority vote over the predictions of 5 of our best models achieving an accuracy of 83.58%.
Machine Learning Tools
TensorFlowWhat's included
| Service Tiers |
Starter
$20
|
Standard
$30
|
Advanced
$50
|
|---|---|---|---|
| Delivery Time | 5 days | 5 days | 5 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 1 | 1 |
Number of Graphs/Charts | 2 | 4 | 5 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - |
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Fast Delivery
+$60 - $100
Additional Revision
+$10
Additional Model Variation
(+ 2 Days)
+$20About Moiz
Artificial Intelligence | Machine learning | Neural network
Islamabad, Pakistan - 1:50 pm local time
🚀 Welcome! I’m Moiz Khan Abbasi, your go-to tech wizard with a flair for turning complex ideas into cutting-edge solutions. Specializing in AI, machine learning, IoT, and computer vision, I’m here to make your tech dreams a reality with a spark of innovation!
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🌫️ IoT-Driven Air Quality Wizardry: I built a game-changing IoT system that doesn’t just monitor air pollution but also predicts future trends. Imagine real-time data transforming into actionable insights for a healthier planet!
🔵 Circle Detection Extraordinaire: Leveraged the Hough Circle Transform to spot and analyze circular shapes in images with pinpoint accuracy. Perfect for advanced computer vision and object recognition!
😷 Face Mask Detection Dynamo: Crafted a state-of-the-art CNN for detecting face masks in real-time. Whether for security or health, my model ensures precise identification and compliance.
📝 Sentiment Analysis Guru: Engineered a powerful sentiment analysis tool using Naive Bayes. Dive deep into text data and extract meaningful insights with stunning accuracy.
🌐Website Design Virtuoso:Designed an eye-catching front-end for a photography website that dazzles with its aesthetic appeal and seamless user experience.
📌 Project Highlights:
📊 Pollution Prediction Powerhouse: Created an IoT-based system that not only tracks but predicts air quality trends, revolutionizing environmental monitoring and public health.
📷Circle Detection Precision: Mastered the art of detecting circles in complex images, enhancing object recognition for advanced applications.
🛡️ Real-Time Mask Detection: Developed a CNN model that excels in detecting face masks, boosting safety and compliance in any setting.
🌟 Stunning Website Design: Delivered a visually stunning and highly functional photography website, blending technical prowess with creative design.
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- "Moiz exceeded all expectations with his innovative solutions and swift execution. A true tech genius!"
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Labeled Dataset
Text samples with sentiment labels.
Format Details:
Dataset format and size