Twitter sentiment Analysis using LSTM
The LSTM Sentiment Analysis project is a deep learning application built to classify Twitter sentiments (positive, negative, neutral) using a multi-layer Long Short-Term Memory (LSTM) neural network. Tweets undergo preprocessing, embedding, and classification through a carefully designed pipeline that balances performance and computation time. A Streamlit interface enables users to input tweets interactively and view predicted sentiments, enhanced with LIME scores that explain the influence of each word. The project demonstrates how LSTMs can capture context and sequence in short texts, making it valuable for social media analytics and customer feedback monitoring.
Client
Personal
Client
Personal
Technology
Deep Learning, Python, Keras
Technology
Deep Learning, Python, Keras
Timeline
5 weeks
Timeline
5 weeks
The project aims to build an accurate and interpretable sentiment analysis system for Twitter by:
Preprocessing tweets (cleaning, lemmatization, removing noise) to prepare structured input.
Embedding text using GloVe + TF-IDF weighted representations to capture semantic meaning.
Designing a multi-layer LSTM architecture with BatchNormalization, Dropout, and L2 regularization for robust predictions.
Evaluating performance through metrics such as accuracy, AUC, F1-score, confusion matrix, and MCC.
Providing a user-friendly Streamlit dashboard for real-time predictions and explanations.
Extending the tool for business applications, including brand monitoring, campaign analysis, and crisis detection.
The LSTM-based model achieved ~64% accuracy and an AUC of ~0.80, performing best on positive and negative tweets while facing challenges with neutral sentiment due to class imbalance and ambiguity. Despite moderate performance, the model provides actionable insights for businesses by tracking brand sentiment, analyzing competitors, and evaluating marketing impact.
Key strengths include:
Balanced trade-off between model accuracy and computation speed (predictions in <20 seconds).
Interpretability through LIME scores.
Scalability with potential CSV batch processing for bulk sentiment analysis.
Future improvements may involve:
Model ensembling or hierarchical classification (neutral vs. non-neutral, then positive vs. negative).
Using newer embeddings (e.g., transformer-based models or OpenAI embeddings).
Enhancing batch sentiment analysis and reporting features for enterprise use.
Overall, this project showcases how deep learning models like LSTMs can power real-world sentiment analysis systems, helping businesses make data-driven, customer-centric decisions.
Twitter sentiment Analysis using LSTM
The LSTM Sentiment Analysis project is a deep learning application built to classify Twitter sentiments (positive, negative, neutral) using a multi-layer Long Short-Term Memory (LSTM) neural network. Tweets undergo preprocessing, embedding, and classification through a carefully designed pipeline that balances performance and computation time. A Streamlit interface enables users to input tweets interactively and view predicted sentiments, enhanced with LIME scores that explain the influence of each word. The project demonstrates how LSTMs can capture context and sequence in short texts, making it valuable for social media analytics and customer feedback monitoring.
Client
Personal
Client
Personal
Technology
Deep Learning, Python, Keras
Technology
Deep Learning, Python, Keras
Timeline
5 weeks
Timeline
5 weeks
The project aims to build an accurate and interpretable sentiment analysis system for Twitter by:
Preprocessing tweets (cleaning, lemmatization, removing noise) to prepare structured input.
Embedding text using GloVe + TF-IDF weighted representations to capture semantic meaning.
Designing a multi-layer LSTM architecture with BatchNormalization, Dropout, and L2 regularization for robust predictions.
Evaluating performance through metrics such as accuracy, AUC, F1-score, confusion matrix, and MCC.
Providing a user-friendly Streamlit dashboard for real-time predictions and explanations.
Extending the tool for business applications, including brand monitoring, campaign analysis, and crisis detection.
The LSTM-based model achieved ~64% accuracy and an AUC of ~0.80, performing best on positive and negative tweets while facing challenges with neutral sentiment due to class imbalance and ambiguity. Despite moderate performance, the model provides actionable insights for businesses by tracking brand sentiment, analyzing competitors, and evaluating marketing impact.
Key strengths include:
Balanced trade-off between model accuracy and computation speed (predictions in <20 seconds).
Interpretability through LIME scores.
Scalability with potential CSV batch processing for bulk sentiment analysis.
Future improvements may involve:
Model ensembling or hierarchical classification (neutral vs. non-neutral, then positive vs. negative).
Using newer embeddings (e.g., transformer-based models or OpenAI embeddings).
Enhancing batch sentiment analysis and reporting features for enterprise use.
Overall, this project showcases how deep learning models like LSTMs can power real-world sentiment analysis systems, helping businesses make data-driven, customer-centric decisions.
Twitter sentiment Analysis using LSTM
The LSTM Sentiment Analysis project is a deep learning application built to classify Twitter sentiments (positive, negative, neutral) using a multi-layer Long Short-Term Memory (LSTM) neural network. Tweets undergo preprocessing, embedding, and classification through a carefully designed pipeline that balances performance and computation time. A Streamlit interface enables users to input tweets interactively and view predicted sentiments, enhanced with LIME scores that explain the influence of each word. The project demonstrates how LSTMs can capture context and sequence in short texts, making it valuable for social media analytics and customer feedback monitoring.
Client
Personal
Client
Personal
Technology
Deep Learning, Python, Keras
Technology
Deep Learning, Python, Keras
Timeline
5 weeks
Timeline
5 weeks
The project aims to build an accurate and interpretable sentiment analysis system for Twitter by:
Preprocessing tweets (cleaning, lemmatization, removing noise) to prepare structured input.
Embedding text using GloVe + TF-IDF weighted representations to capture semantic meaning.
Designing a multi-layer LSTM architecture with BatchNormalization, Dropout, and L2 regularization for robust predictions.
Evaluating performance through metrics such as accuracy, AUC, F1-score, confusion matrix, and MCC.
Providing a user-friendly Streamlit dashboard for real-time predictions and explanations.
Extending the tool for business applications, including brand monitoring, campaign analysis, and crisis detection.
The LSTM-based model achieved ~64% accuracy and an AUC of ~0.80, performing best on positive and negative tweets while facing challenges with neutral sentiment due to class imbalance and ambiguity. Despite moderate performance, the model provides actionable insights for businesses by tracking brand sentiment, analyzing competitors, and evaluating marketing impact.
Key strengths include:
Balanced trade-off between model accuracy and computation speed (predictions in <20 seconds).
Interpretability through LIME scores.
Scalability with potential CSV batch processing for bulk sentiment analysis.
Future improvements may involve:
Model ensembling or hierarchical classification (neutral vs. non-neutral, then positive vs. negative).
Using newer embeddings (e.g., transformer-based models or OpenAI embeddings).
Enhancing batch sentiment analysis and reporting features for enterprise use.
Overall, this project showcases how deep learning models like LSTMs can power real-world sentiment analysis systems, helping businesses make data-driven, customer-centric decisions.