Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media
According to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extrac...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-01-01
|
| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2019.1673037 |
| _version_ | 1797684881665294336 |
|---|---|
| author | A. E. O. Carosia G. P. Coelho A. E. A. Silva |
| author_facet | A. E. O. Carosia G. P. Coelho A. E. A. Silva |
| author_sort | A. E. O. Carosia |
| collection | DOAJ |
| description | According to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extraction. The technique known as Sentiment Analysis (SA) aims to analyze opinions, sentiments, and emotions present in unstructured data, leading many papers to address the impact of news and social media publications on the financial market. However, the literature lacks works considering the effects of sentiment available on social media and its impacts on the Brazilian stock market. This work aims to conduct a study of the Brazilian stock market movement through SA in Twitter considering tree perspectives: (i) absolute number of tweet sentiments; (ii) tweets sentiments weighted by favorites; and (iii) tweets sentiments weighted by retweets. The analyzed period was the Brazilian electoral period of 2018 (01-Oct-2018 to 31-Dec-2018). In this paper, we first developed a comparison study with SA Machine Learning techniques (Naive Bayes, Support Vector Machines, Maximum Entropy, and Multilayer Perceptron) and then applied the best algorithm to establish the relations between sentiments and the Brazilian stock market movement considering different time frames (windows sizes). Results indicate that Multilayer Perceptron was the best technique to perform SA in Portuguese. In addition, we observed that the predominant sentiment in social media relates to the stock market movement, improving accuracy as long as windows sizes are increased. |
| first_indexed | 2024-03-12T00:36:10Z |
| format | Article |
| id | doaj.art-6fd2c4245bce4777915f773578d64ec1 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-12T00:36:10Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-6fd2c4245bce4777915f773578d64ec12023-09-15T09:33:57ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452020-01-0134111910.1080/08839514.2019.16730371673037Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social MediaA. E. O. Carosia0G. P. Coelho1A. E. A. Silva2Federal Institute of São Paulo (IFSP)University of Campinas (UNICAMP)University of Campinas (UNICAMP)According to the Efficient Market Hypothesis, financial market movements are dependent on news and external events that have a significant impact on the market value of companies. Thus, a great amount of applications has arisen to explore this knowledge through automatic sentiment and opinion extraction. The technique known as Sentiment Analysis (SA) aims to analyze opinions, sentiments, and emotions present in unstructured data, leading many papers to address the impact of news and social media publications on the financial market. However, the literature lacks works considering the effects of sentiment available on social media and its impacts on the Brazilian stock market. This work aims to conduct a study of the Brazilian stock market movement through SA in Twitter considering tree perspectives: (i) absolute number of tweet sentiments; (ii) tweets sentiments weighted by favorites; and (iii) tweets sentiments weighted by retweets. The analyzed period was the Brazilian electoral period of 2018 (01-Oct-2018 to 31-Dec-2018). In this paper, we first developed a comparison study with SA Machine Learning techniques (Naive Bayes, Support Vector Machines, Maximum Entropy, and Multilayer Perceptron) and then applied the best algorithm to establish the relations between sentiments and the Brazilian stock market movement considering different time frames (windows sizes). Results indicate that Multilayer Perceptron was the best technique to perform SA in Portuguese. In addition, we observed that the predominant sentiment in social media relates to the stock market movement, improving accuracy as long as windows sizes are increased.http://dx.doi.org/10.1080/08839514.2019.1673037 |
| spellingShingle | A. E. O. Carosia G. P. Coelho A. E. A. Silva Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media Applied Artificial Intelligence |
| title | Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media |
| title_full | Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media |
| title_fullStr | Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media |
| title_full_unstemmed | Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media |
| title_short | Analyzing the Brazilian Financial Market through Portuguese Sentiment Analysis in Social Media |
| title_sort | analyzing the brazilian financial market through portuguese sentiment analysis in social media |
| url | http://dx.doi.org/10.1080/08839514.2019.1673037 |
| work_keys_str_mv | AT aeocarosia analyzingthebrazilianfinancialmarketthroughportuguesesentimentanalysisinsocialmedia AT gpcoelho analyzingthebrazilianfinancialmarketthroughportuguesesentimentanalysisinsocialmedia AT aeasilva analyzingthebrazilianfinancialmarketthroughportuguesesentimentanalysisinsocialmedia |