Developed a sentiment detection model using many-to-one Long Short-Term Memory (LSTM) neural networks to accurately predict sentiment labels from airline customer reviews. Implemented data preprocessing techniques, including text cleaning and conversion to a bag-of-words representation, leading to the successful training and evaluation of the model on a dataset containing sentiment labels and text reviews.
Leveraged many-to-one LSTMs to generate contextually relevant text by training on "Alice's Adventures in Wonderland." Addressed the challenges of text generation through entropy scaling and softmax temperature adjustment, enabling the creation of coherent and diverse text outputs based on next-word prediction.
Utilized Python and libraries such as pandas, numpy, keras, tensorflow, and nltk to preprocess data, train LSTM models, and implement solutions to overcome the complexities of natural language processing. Demonstrated the ability to navigate the dynamic nature of language and the intricacies of sentiment analysis and text generation, enhancing skills in machine learning and deep learning applications.