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The repository focuses on developing a comprehensive business opportunity analysis system that uses geospatial data, sentiment analysis, and topic modeling. The objective is to leverage these techniques to identify and evaluate potential business opportunities in area of interest.
The repository focuses on developing a comprehensive business opportunity analysis system that uses geospatial data, sentiment analysis, and topic modeling. The objective is to leverage these techniques to identify and evaluate potential business opportunities in area of interest.
This project employs a deep neural network architecture for the classification of toxic comments, utilizing the Kaggle competition dataset from the Jigsaw Toxic Comment Classification Challenge.
Sentiment analysis on IMDB movie reviews using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) cells for binary classification of positive and negative sentiments.
The primary objective of this project is to develop a robust system capable of accurately classifying patient conditions solely based on their reviews. By leveraging advanced NLP techniques, the project aims to streamline the categorization process and provide valuable insights into patient health status.
Repo containing Channel Quality Indicator (CQI) data from real car routes in Greece. It contains a reproducable notebook with the implementation of a Bidirectional LSTM Neural Network for real-time CQI forecasting in heterogeneous ultra-dense beyond-5G networks.
Detect various kind of data of badminton matches using Bidirectional LSTM || AI Cup 2023 - Teaching Computers to Watch Badminton Matches (5th place) / 教電腦看羽球 (第五名)
Utilizing advanced Bidirectional LSTM RNN technology, our project focuses on accurately predicting stock market trends. By analyzing historical data, our system learns intricate patterns to provide insightful forecasts. Investors gain a robust tool for informed decision-making in dynamic market conditions. With a streamlined interface, our solution
I crafted a robust sentiment analysis model featuring Bidirectional LSTM layers and embeddings. Leveraging a Sequential model structure and fine-tuning with 'sparse_categorical_crossentropy' loss and 'adam' optimizer, the implementation excels in capturing contextual nuances for precise emotion classification in natural language data.
Detect and classify toxic behavior in social media comments using a bidirectional LSTM-based neural network. Achieved precision of 0.932 and recall of 0.733. Applications include customer service, reputation management, and market research. Real-time predictions available via a Gradio app. Future scope includes multi-lingual sentiment analysis.