Sentiment analysis is the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.
Word2vec is a technique for NLP. This algorithm uses a neural network model to learn word associations. This may also be called word embedding in various texts. For our purpose and to make the project easy, we will make use of Gensim, which will help us to develop word embeddings.
LSTM (Long short-term memory) are special kinds of RNN, these are designed to have a long-term memory making them capable of understanding the context better. Will these help us understand the context or sentiment of our tweets better, or not?