# Financial Context News Sentiment Analysis for the Lithuanian Language
## 2 Related Works  
These days, the most common solution for a smart system is the application of artificial intelligence (AI) techniques. AI helps to solve various tasks, such as classification, prediction, clustering, etc. One sub-area of artificial intelligence is machine learning (ML). The main aim of ML in practice is to analyze data, learn from obtained results, and then make a decision or prediction about something in the analyzed area. The more the ML model learns by feeding it data, the better the results will be. Nowadays, machine learning models are used in different areas [9; 10; 11], but in all of them, the main model concept is generally the same. There are various research studies on using machine learning for sentiment analysis. For example, the Twitter sentiment analysis was performed using a sentiment analysis model based on Naive Bayes and Support Vector Machine [12]. In other research, the experiments were made using five machine learning algorithms to run sentiment analyses of movie reviews [13]. Sentiment analysis solutions are used to predict the stock market price based on Twitter message sentiment as well [14]. They all had the same task--to find the best model for a specific dataset, but results can be obtained in various ways. Machine learning models, according to the learning style, can be grouped as supervised, unsupervised, and hybrid learning, where the main factor of choosing one of them is the analyzed dataset (classified data, unclassified data). Sentiment analysis is a classification task that attempts to predict whether a positive or negative emotion is expressed in a text. Generally, it is a binary classification task--the text is assigned to one of two classes (positive or negative), but often, as in this paper, the third class is also relevant--a neutral opinion. In that case, it is considered the multiclass classification task. Sentiment analysis can be applied in different areas, such as user opinions on received products or movies recommendations, according to the user comments, etc. However, financial or business area texts have their specifics, which have to be taken into account. General-purpose models are not effective enough, as some specialized term and event text style is used in a financial context. Therefore, close to general-purpose models for language understanding [15], pre-trained language models for the financial area arise [16]. In 2018, [PERSON] published a paper [17] in which he tried to extract sentiment from user messages on the financial social platform StockTwits. In other words, an attempt was made to predict whether the stock price would increase or not (binary classification) based on sentiments of user messages using traditional machine learning (doc2 vec) and deep learning (LSTM and CNN) methods. The results showed that doc2 vec is not effective in extracting sentiment from the large financial news dataset and an accuracy of 0.67623 was achieved. Long short-term memory (LSTM) was constructed using the average pooling layer and logistic regression layer in the last step. This solution also managed to achieve an accuracy of 0.6923. The best accuracy was achieved with a convolutional neural network (CNN)--0.9093 in 10.000 steps. Filters with sizes of 3, 4, and 5, max-pooling layer, dropout regularization, and softmax as the last layer were used to construct this model. The result of CNN was quite high, but it should be noted that only two classes were used in this classificatory. Comparative analysis of machine learning methods to extract sentiments from user messages on the StockTwits platform was performed by [PERSON] [18]. The research was conducted with different sizes of balanced and unbalanced data and the results showed that the size of the dataset had a greater effect on accuracy than the choice of method. It was also shown that accuracy does not improve significantly when text features are composed of more than two words (bigrams). The highest accuracy was obtained with the maximum entropy classifier--0.74451.