Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers
Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of posi...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9142175/ |
| _version_ | 1819175779067494400 |
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| author | Kostadin Mishev Ana Gjorgjevikj Irena Vodenska Lubomir T. Chitkushev Dimitar Trajanov |
| author_facet | Kostadin Mishev Ana Gjorgjevikj Irena Vodenska Lubomir T. Chitkushev Dimitar Trajanov |
| author_sort | Kostadin Mishev |
| collection | DOAJ |
| description | Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, we design an evaluation platform which we use to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers. We perform more than one hundred experiments using publicly available datasets, labeled by financial experts. We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers. The results show improved efficiency of contextual embeddings in sentiment analysis compared to lexicons and fixed word and sentence encoders, even when large datasets are not available. Furthermore, distilled versions of NLP transformers produce comparable results to their larger teacher models, which makes them suitable for use in production environments. |
| first_indexed | 2024-12-22T21:00:17Z |
| format | Article |
| id | doaj.art-04306a91a10046efac0099e73dc76926 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:00:17Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-04306a91a10046efac0099e73dc769262022-12-21T18:12:50ZengIEEEIEEE Access2169-35362020-01-01813166213168210.1109/ACCESS.2020.30096269142175Evaluation of Sentiment Analysis in Finance: From Lexicons to TransformersKostadin Mishev0https://orcid.org/0000-0003-3982-3330Ana Gjorgjevikj1https://orcid.org/0000-0002-5135-7718Irena Vodenska2https://orcid.org/0000-0003-1183-7941Lubomir T. Chitkushev3Dimitar Trajanov4https://orcid.org/0000-0002-3105-6010Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North MacedoniaFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North MacedoniaFinancial Informatics Lab, Metropolitan College, Boston University, Boston, MA, USAFinancial Informatics Lab, Metropolitan College, Boston University, Boston, MA, USAFaculty of Computer Science and Engineering, Ss. Cyril and Methodius University, Skopje, North MacedoniaFinancial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, we design an evaluation platform which we use to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers. We perform more than one hundred experiments using publicly available datasets, labeled by financial experts. We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers. The results show improved efficiency of contextual embeddings in sentiment analysis compared to lexicons and fixed word and sentence encoders, even when large datasets are not available. Furthermore, distilled versions of NLP transformers produce comparable results to their larger teacher models, which makes them suitable for use in production environments.https://ieeexplore.ieee.org/document/9142175/Sentiment analysisfinancenatural language processingtext representationsdeep-learningencoders |
| spellingShingle | Kostadin Mishev Ana Gjorgjevikj Irena Vodenska Lubomir T. Chitkushev Dimitar Trajanov Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers IEEE Access Sentiment analysis finance natural language processing text representations deep-learning encoders |
| title | Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers |
| title_full | Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers |
| title_fullStr | Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers |
| title_full_unstemmed | Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers |
| title_short | Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers |
| title_sort | evaluation of sentiment analysis in finance from lexicons to transformers |
| topic | Sentiment analysis finance natural language processing text representations deep-learning encoders |
| url | https://ieeexplore.ieee.org/document/9142175/ |
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