Real-Time Stock Trend Prediction via Sentiment Analysis of News Article

The stock market is volatile and volatility occurs in clusters, price fluctuations based on sentiment and news reports are common. A trader uses a wide variety of publicly available information to forecast the marketing decision. This paper proposes an advice to traders for stock trading using sentimental analysis of publically available news reports. It is based on a hypothesis, that news articles have an impact on the stock market, with this hypothesis we study the relationship between news and stock trend and also proved that negative news has a persistent effect on the stock market. In order to prove this assumption semi-supervised learning technique is being used to build the final model of news classification. This research shows that SVM with TF-IDF as feature performs well in further analysis. The accuracy of the prediction model is more than 90% having 52% correlation with the return label of a stock. This paper also proposes a real-time system which fetches news of any company on a real-time basis and displays its top five news and also predicts the adjusted close price of the next seven days. Keywords: Text Mining, Human Sentiments, KNN, Random Forest, Multinomial Naïve Bayes, linear SVM, News

Predicting the Effects of News Sentiments on the Stock Market

Stock market forecasting is very important in the planning of business activities. Stock price prediction has attracted many researchers in multiple disciplines including computer science, statistics, economics, finance, and operations research. Recent studies have shown that the vast amount of online information in the public domain such as Wikipedia usage pattern, news stories from the mainstream media, and social media discussions can have an observable effect on investors opinions towards financial markets. The reliability of the computational models on stock market prediction is important as it is very sensitive to the economy and can directly lead to financial loss. In this paper, we retrieved, extracted, and analyzed the effects of news sentiments on the stock market. Our main contributions include the development of a sentiment analysis dictionary for the financial sector, the development of a dictionary-based sentiment analysis model, and the evaluation of the model for gauging the effects of news sentiments on stocks for the pharmaceutical market. Using only news sentiments, we achieved a directional accuracy of 70.59% in predicting the trends in short-term stock price movement.Comment: 4 page

Semantic Sentiment Analysis of Twitter Data

Internet and the proliferation of smart mobile devices have changed the way information is created, shared, and spreads, e.g., microblogs such as Twitter, weblogs such as LiveJournal, social networks such as Facebook, and instant messengers such as Skype and WhatsApp are now commonly used to share thoughts and opinions about anything in the surrounding world. This has resulted in the proliferation of social media content, thus creating new opportunities to study public opinion at a scale that was never possible before. Naturally, this abundance of data has quickly attracted business and research interest from various fields including marketing, political science, and social studies, among many others, which are interested in questions like these: Do people like the new Apple Watch? Do Americans support ObamaCare? How do Scottish feel about the Brexit? Answering these questions requires studying the sentiment of opinions people express in social media, which has given rise to the fast growth of the field of sentiment analysis in social media, with Twitter being especially popular for research due to its scale, representativeness, variety of topics discussed, as well as ease of public access to its messages. Here we present an overview of work on sentiment analysis on Twitter.Comment: Microblog sentiment analysis; Twitter opinion mining; In the Encyclopedia on Social Network Analysis and Mining (ESNAM), Second edition. 201

Natural language processing and financial markets: semi-supervised modelling of coronavirus and economic news

Este documento estudia las reacciones de los mercados financieros de Estados Unidos a nuevas noticias de la prensa desde enero de 2019 hasta el primero de mayo de 2020. Con este fin, construimos medidas del contenido y del sentimiento de las noticias mediante el desarrollo de índices apropiados a partir de los titulares y fragmentos de The New York Times, utilizando técnicas de aprendizaje automático no supervisado. En particular, usamos el modelo Asignación Latente de Dirichlet para inferir el contenido (temas) de los artículos, y Word Embedding (implementado con el modelo Skip-gram) y K-Medias para medir su sentimiento (incertidumbre). De esta forma, elaboramos un conjunto de índices de incertidumbre temáticos diarios. Estos índices se utilizan luego para explicar el comportamiento de los mercados financieros de Estados Unidos mediante la implementación de un conjunto de modelos EGARCH. En conclusión, encontramos que dos de los índices de incertidumbre temáticos (uno relacionado con noticias del COVID-19 y otro con noticias de la guerra comercial) explican gran parte de los movimientos en los mercados financieros desde principios de 2019 hasta los cuatro primeros meses de 2020. Además, encontramos que el índice de incertidumbre temático relacionado con la economía y la Reserva Federal está positivamente relacionado con los mercados financieros, capturando las acciones de la Reserva Federal durante períodos de incertidumbre.This paper investigates the reactions of US financial markets to press news from January 2019 to 1 May 2020. To this end, we deduce the content and sentiment of the news by developing apposite indices from the headlines and snippets of The New York Times, using unsupervised machine learning techniques. In particular, we use Latent Dirichlet Allocation to infer the content (topics) of the articles, and Word Embedding (implemented with the Skip-gram model) and K-Means to measure their sentiment (uncertainty). In this way, we arrive at the definition of a set of daily topic-specific uncertainty indices. These indices are then used to find explanations for the behaviour of the US financial markets by implementing a batch of EGARCH models. In substance, we find that two topic-specific uncertainty indices, one related to COVID-19 news and the other to trade war news, explain the bulk of the movements in the financial markets from the beginning of 2019 to end-April 2020. Moreover, we find that the topic-specific uncertainty index related to the economy and the Federal Reserve is positively related to the financial markets, meaning that our index is able to capture actions of the Federal Reserve during periods of uncertainty

The Development of a Temporal Information Dictionary for Social Media Analytics

Dictionaries have been used to analyze text even before the emergence of social media and the use of dictionaries for sentiment analysis there. While dictionaries have been used to understand the tonality of text, so far it has not been possible to automatically detect if the tonality refers to the present, past, or future. In this research, we develop a dictionary containing time-indicating words in a wordlist (T-wordlist). To test how the dictionary performs, we apply our T-wordlist on different disaster related social media datasets. Subsequently we will validate the wordlist and results by a manual content analysis. So far, in this research-in-progress, we were able to develop a first dictionary and will also provide some initial insight into the performance of our wordlist