🚨 Just a heads up, the description is generated by GPT. Yep, those same GPTs that will one day take over the world. But don't worry, for now, they're content with generating descriptions! 😂 But the Training and Evaluation matrices are correct.
Title: Deep Learning Twitter Sentiment Analysis Model
In the vast sea of social media, Twitter stands out as a microblogging platform where individuals express their thoughts, feelings, and opinions in 280 characters or less. Within this dynamic ecosystem, sentiment analysis plays a pivotal role in deciphering the underlying emotions behind tweets. Enter our monumental creation – the Deep Learning Twitter Sentiment Analysis Model.
Overview:
Our model is a testament to the power of deep learning in understanding the intricate nuances of human language. Trained on a colossal dataset comprising 537,000 tweets, it has mastered the art of discerning sentiment with unparalleled accuracy. Leveraging the TensorFlow framework, this neural network is a formidable force in the realm of natural language processing.
Dataset:
Sentiment140 dataset with 1.6 million tweets form kaggle
Data Preprocessing:
Before delving into the intricacies of deep learning, the journey begins with meticulous data preprocessing. We embarked on a quest to cleanse and prepare our dataset for the neural network's consumption. Using the Polars library for data manipulation and TensorFlow for tokenization, we meticulously scrubbed the data of extraneous elements such as user mentions and non-alphanumeric characters.
Model Architecture:
At the heart of our creation lies a sophisticated architecture designed to unravel the sentiment concealed within each tweet. The model features a sequential arrangement of layers, each meticulously crafted to extract and distill the essence of textual data.
Embedding Layer: Acting as the gateway to semantic understanding, the embedding layer transforms words into dense vectors, capturing their contextual meaning.
Bidirectional LSTM Layers: Embracing the temporal dynamics of language, our model employs bidirectional Long Short-Term Memory (LSTM) layers to capture dependencies in both forward and backward directions. With 64 units in the first LSTM layer and 32 units in the second, the network delves deep into the temporal context of tweets.
Dropout Layer: To prevent overfitting and enhance generalization, a dropout layer with a dropout rate of 0.5 is strategically placed to stave off the curse of dimensionality.
Dense Layer with Sigmoid Activation: Culminating in a dense layer with a sigmoid activation function, the model synthesizes the extracted features to deliver a probabilistic prediction of sentiment.
Training and Evaluation:
After 100 epochs
loss: 0.0199
accuracy: 0.9917
val_loss: 0.3046
val_accuracy: 0.9300
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