Deep learning for financial sentiment analysis on finance news providers

Day, Min-Yuh; Lee, Chia-Chou

doi:10.1109/asonam.2016.7752381

Cited by 96 publications

(47 citation statements)

References 10 publications

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“…however, little research has been done on social media and its effects on forecast results in pandemics seasons. In this regard, Day and Lee [10] argued that nancial sentiment analysis is an important area of nancial technology research (FinTech). Chen and colleagues [8] found that social media sentiment associates strongly with stock returns and identi ed the importance of social media as an additional channel to re ect stock prices.…”

Section: Sentiment Analysis and Financementioning

confidence: 99%

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Valle-Cruz

Fernandez-Cortez

López-Chau

et al. 2020

Preprint

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Abstract Backgroud:Investors are always playing with the fears and desires of buyers and sellers. Stock exchange markets are not the exception. Financial sentiment analysis allows us to understand the effect of reactions and emotions on social media in the stock market. In this research, we analyze Twitter data and financial indices to answer the question: How do polarity generated by the posts on Twitter influence financial indices behavior in pandemic seasons? Methods:The study is based on the sentiment analysis of influential Twitter accounts in this field and its relationship with the behavior of important financial indices. To achieve this, we tested four lexicons to detect polarity on Twitter. Results:Our findings shows that the period in which the markets reacted was 6 to 13 days after the information was shared and disseminated on Twitter in the COVID-19 season, and 1 to 2 day for H1N1 season. Furthermore, in our analysis, we found that the lexicons that got the best results for sentiment analysis on Twitter were S140 and Affin.Conclusions:Financial sentiment analysis is an important technique to forecasting stock market and polarity is the most widely used technique in the financial area. There is a relationship between the polarity in Twitter and the financial indexes behavior. The most influential Twitter accounts during the pandemic season were The New York Times, Bloomberg, CNN News, and Investing, presenting a very high relation between sentiments on Twitter and the stock market behavior.

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Section: Sentiment Analysis and Financementioning

confidence: 99%

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Valle-Cruz

Fernandez-Cortez

López-Chau

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This can be done through machine learning techniques like conditional random fields referenced in Tseng et al (2005). Some open source NLP tools available for this purpose include the Jieba [6] and the excellent Stanford NLP program. After the Chinese phrases are extracted by word segmentation, typical stop words are removed referencing a standard corpus.…”

Section: Chinese Natural Language Processingmentioning

confidence: 99%

An NLP-PCA Based Trading Strategy On Chinese Stock Market

Liu¹,

Zhu²,

Chong³

2019

Proceedings of the 4th International Conference on Humanities Science, Management and Education Technology (HSMET 2019)

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The stock market is a barometer of a country's economy. However, the stock market is significantly affected by policies, news, and public opinion, and it is prone to volatility. Compared with the already mature financial securities market in foreign countries, China's stock market is still in the exploratory stage. There are more individual stock speculators in short-term trading. They will search for news through various channels to make decisions. Behavioral finance has created a theoretical basis for the mining of stock reviews. The rise of technologies such as text mining, machine learning, and time series models has made stock review mining possible. In this paper, we extract 28 kinds of financial sentiment features from thousands of Chinese news and social media outlets by NLP. Compared with popular methods where they only use positive or negative sentiment to predict stock price, we find out five more specific information categories from the news, which is SSE rising or dropping expectation, macro-public finance, bond sentiment, bond price forecast, the buzz on bond, rate and stock index. Finally, we predict stock price using these primary factors, providing a specific predictive ability to the stock market trend.

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“…Deep Learning was firstly proposed by G.E. Hinton in 2006 and is the part of machine learning process which refers to Deep Neural Network [7]. Neural network is influenced by human brain and it contains several neurons that make an impressive network.…”

Section: B Deep Learningmentioning

confidence: 99%

Sentiment Analysis Using Deep Learning Techniques: A Review

Ain¹,

Ali²,

Riaz³

et al. 2017

ijacsa

108

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The World Wide Web such as social networks, forums, review sites and blogs generate enormous heaps of data in the form of users views, emotions, opinions and arguments about different social events, products, brands, and politics. Sentiments of users that are expressed on the web has great influence on the readers, product vendors and politicians. The unstructured form of data from the social media is needed to be analyzed and well-structured and for this purpose, sentiment analysis has recognized significant attention. Sentiment analysis is referred as text organization that is used to classify the expressed mind-set or feelings in different manners such as negative, positive, favorable, unfavorable, thumbs up, thumbs down, etc. The challenge for sentiment analysis is lack of sufficient labeled data in the field of Natural Language Processing (NLP). And to solve this issue, the sentiment analysis and deep learning techniques have been merged because deep learning models are effective due to their automatic learning capability. This Review Paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis, etc.

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Deep learning for financial sentiment analysis on finance news providers

Cited by 96 publications

References 10 publications

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

Does Twitter affect Stock Market Decisions?Financial Sentiment Analysis in Pandemic Seasons: A Comparative Study of H1N1 and COVID-19

An NLP-PCA Based Trading Strategy On Chinese Stock Market

Sentiment Analysis Using Deep Learning Techniques: A Review

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