Abstract
Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).
Highlights
In the business field, companies are always attempting to discover the best solutions on how to optimize the business process by implementing new technologies
Sentiment analysis is the field of data analysis, where the main aim is to assign the sentiment to unknown texts, usually positive, negative, or neutral, or in other words, emotion extraction [1]
The results obtained algorithm was more sensitive to the dataset preparation compared to the Naive Bayes using the long short-term memory algorithm showed that the highest accuracy (71.1%)
Summary
Companies are always attempting to discover the best solutions on how to optimize the business process by implementing new technologies. Sentiment analysis can be used in various fields, such as analysis of customer reviews [2], YouTube user comments [3], social networks [4], websites news [5], etc., but the main concept of sentiment analysis is the same—to retrieve the short texts and assign the sentiment. The goal of the lexicon-based method [6] is to prepare the positive and negative list of words, and later, use tagging algorithms to assign the values (positive, negative) to the words in the texts. In such a way, according to the tagged word frequency, the score is calculated and the sentiment is assigned.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
