News Analytics for Business Sentiment Suggestion

Abstract

Business and economics news has become one of the factors businesses consider when making decisions. However, the exponential increase in the availability of business information sources on the internet makes it more difficult for entrepreneurs to keep up with and extract useful insights from many news articles. Although many preceding works focused on the sentiment extracted in the news, the results were intended for everyone. The sentiments based on a user's queries are needed to provide customized service. Hence, this paper proposed a system integrated into a chatbot to automatically understand users' queries and recommend sentiments based on news articles. The main objective is to provide entrepreneurs, especially those considering international trade and investment, with the sentiments embodied in the latest news articles to help them keep up with the business and economic trends relevant to them. The methodology is based on deep learning and transfer learning. A pre-trained deep learning model was fine-tuned for natural language processing tasks to perform sentiment analysis in news articles. A survey questionnaire was used to measure the effectiveness of the system. The survey result showed that most users agreed with the predicted sentiments from the system.