# Financial Context News Sentiment Analysis for the Lithuanian Language
###### 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%).  
sentiment analysis; financial news analysis; supervised machine learning; Lithuanian language +
Footnote †: journal: Medical  
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. The main problem of this method is to correctly prepare the lists and to find the best score, which allows separating datasets into the sentiment groups, especially when the neutral sentiment is used. To use the classification algorithms [7], the main aim is to correctly prepare and preprocess the dataset, and preferably use a large number of dataset items to train a model. The various classifiers and their parameters can be used to obtain the best results, so deep analysis must be conducted.  
The difficulty of sentiment analysis is that, even for a human, it is sometimes hard to decide the difference among positive, negative, and neutral texts. Usually, in the same text, there can be two sentiments, so sentiment analysis accuracy is often not so high. In this paper, both methods were used to assign the sentiment analysis of financial context Lithuanian news. The newly collected dataset from four main Lithuanian news websites were collected and experimentally analyzed [8]. There is no database where this kind of information would be freely accessed. Most sentiment datasets are oriented on comment sentiment estimation; therefore, it is unknown how accurate sentiment can be estimated on financial news topics published on websites. Therefore the paper's main contribution is datasets for sentiment analysis for financial news, as well as research on how effective existing classification methods are for sentiment estimation with this dataset.  
To perform the lexicon-based approach we used the positive and negative bigrams prepared with financial company experts. The supervised machine learning model was implemented to discover the best classifier for our collected dataset. The multinomial Naive Bayes, support vector machine, and a long short-term memory algorithm were analyzed and compared. To evaluate the results, the model accuracy was calculated. Moreover, hyperparameters optimization was used to discover the best parameters for each classifier. In Section 2, the related works are analyzed. The implemented supervised machine model for sentiment analysis are described and presented in Section 3. Moreover, in this section, the newly collected dataset is described. Finally, the experimental investigation and results comparison are presented in Section 4.