Stock Price Prediction Using News Sentiment Analysis

Abstract

In this era of digitization, tasks can be performed from anywhere in the world that previously required manual movement. It is the same for investing and trading in stocks. With the ease of investing and trading in stocks via the internet, a more extensive segment of society has started investing. The stock price depends on multiple factors such as politics, economics, war, society, and news sentiment. Therefore stocks are really hard to predict due to such vast dependencies. Stock markets are an important issue in the financial world. Prediction of stock prices during the global pandemic of Novel Coronavirus 2019 (COVID-19) can be very helpful to stakeholders. The attempt of predicting the stock prices have been made by previous researchers using sentimental news analysis through Support Vector Machine (SVM), Neural Network, and Naive Bayes. However, they have low accuracy, and some even claim that news is not a crucial governing factor for the stock price. This paper aims to predict the stock market prices through news sentimental analysis using techniques such as Long Short Term Memory and Artificial Neural Network against classifier models like Natural Language Toolkit, Valence Aware Dictionary for Sentiment Reasoning, Recurrent Neural Network for price prediction. S.Mohan [1] MAPE scores came out to be 1.17, 2.43 for RNN and RNN with news polarity for Facebook stock prices. Our results came out to be 1.21 and 1.94, slightly better results, thus showing optimism in the dependence of stock prices on the news.