Evaluation of News Sentiment in Economic Activity Forecasting

Abstract

Natural language processing is a rapidly expanding field of artificial intelligence, the main goal of which is linguistics. This field allows various mathematical/computer science techniques to be applied to natural language processing. Sentiment analysis is one of the most common tasks that is solved based on natural language processing. The primary purpose of sentiment analysis is to determine the mood (happy, sad, angry, and others) or polarity (negative, neutral, positive) of the presented text. Based on the relevance of the application of natural language processing, this study aims to create a dataset of Lithuanian news and determine the sentiment of this news. Identified news sentiment is associated with different indicators of economic activity. More than 1 million articles (1,256,227) have been collected from the largest news portal. The articles were collected from the first month of 2006 to the fifth month of 2022. More than 20,000 different LSTM, GRU, and RNN models were built with different parameters and datasets (univariate, individual sentiments, all sentiments, clusters). Based on the obtained results, it can be observed that the inclusion of sentiments in clustering increased the accuracy of forecasting different economic activity indicators. The highest accuracy in all cases was obtained based on the best sentiment for individual time series.

Keywords

• clustering
• economic activity
• natural language processing
• NLP
• transformers
• BERT