  Investors make investment decisions depending on several factors such as
fundamental analysis, technical analysis, and quantitative analysis. Another
factor on which investors can make investment decisions is through sentiment
analysis of news headlines, the sole purpose of this study. Natural Language
Processing techniques are typically used to deal with such a large amount of
data and get valuable information out of it. NLP algorithms convert raw text
into numerical representations that machines can easily understand and
interpret. This conversion can be done using various embedding techniques. In
this research, embedding techniques used are BoW, TF-IDF, Word2Vec, BERT,
GloVe, and FastText, and then fed to deep learning models such as RNN and LSTM.
This work aims to evaluate these model's performance to choose the robust model
in identifying the significant factors influencing the prediction. During this
research, it was expected that Deep Leaming would be applied to get the desired
results or achieve better accuracy than the state-of-the-art. The models are
compared to check their outputs to know which one has performed better.
