Abstract
Due to market volatility, forecasting stock market trend is one of the most complicated tasks. There is a heated debate about the effectiveness of predicting market movement based on public sentiments expressed in written news, whether through social media, web pages, or financial news. However, past researches ignore a crucial source of knowledge, which is videos news, that is typically introduced by market specialists. This research examined the reliability of using the sentiment of video news sites to forecast the stock price. To determine the strength of the causal relationship between stock market prices and video sentiments, we applied Granger causality analysis and Pearson correlation coefficient tests. We also investigated the use of TextBlob API versus the efficiency of Google Cloud Natural Language API to find sentiment polarity scores for video news. Various models were evaluated for Sentiment Analysis of S&P 500 stock using LR, SVM, LSTM, and CNN models. Finally, we utilized the most effective sentiment analysis tool to train our ML classification model. This research is unique because it identifies and tests the question. Can we build an effective prediction model based on video news sentiment or can we add video news sentiment as a new feature to our future prediction model? The experimental findings demonstrate that there is a causal connection between video news sentiment and stock market fluctuation. The findings also revealed that when using the Google Cloud Natural Language API for sentiment analysis, the model showed a correlation between the video news and the company's price movements.
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