Sentiment Analysis of Financial Textual data Using Machine Learning and Deep Learning Models
Recently, extensive research in the field of financial sentiment analysis has been conducted. Sentiment analysis (SA) of any text data denotes the feelings and attitudes of the individual on particular topics or products. It applies statistical approaches with artificial intelligence (AI) algorithms to extract substantial knowledge from a huge amount of data. This study extracts the Sentiment polarity (negative, positive, and neutral) from financial textual data using machine learning and deep learning algorithms. The constructed machine learning model used Multinomial Naïve Bayes (MNB) and Logistic regression (LR) classifiers. On the other hand, three deep learning algorithms have been utilized which are Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results of the MNB and LR are obtained good and very good rate of accuracy respectively. Likewise, the results of RNN, LSTM and GRU obtained an excellent rate of accuracy. It can be concluded from the outcomes that the used preprocessing stages made a positive impact on the accuracy rate.
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