HakimGhlissi / Twitter-Sentiment-Analysis-using-GloVe-Word-Embedding-and-LSTM

Built a Twitter Sentiment Analysis tool using a GloVe word embedding vectors and LSTM Neural Networks from data out of a Kaggle dataset

Twitter-Sentiment-Analysis-using-GloVe-Word-Embedding-and-LSTM

Built a Twitter Sentiment Analysis tool using a GloVe word embedding vectors and LSTM Neural Networks from data out of a Kaggle dataset

Dataset Source : https://www.kaggle.com/datasets/arkhoshghalb/twitter-sentiment-analysis-hatred-speech

Methods Used:

GloVe: Global Vectors for Word Representation:

What is GloVe method? The GloVe method is a type of linear regression that uses contraction. GloVe is an unsupervised learning algorithm used to obtain vector representations of words. The learning process is conducted on the aggregation of the global words and the co-occurrence statistics of the words in the given corpus, with the resulting representation displaying an interesting linear substructure of the vector space of words.

LSTM Neural Networks:

What does an LSTM do? LSTM networks are well-suited for classification, processing, and prediction based on time-series data because of the potential delays of unknown periods between important time-series events.
LSTMs are designed to overcome vanishing gradient problems that can occur when training traditional RNNs.