Applying machine learning to study correlations, if any, between news content and stock price movements
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. Description:'Text classification problems are quite successfully solved by current machine learning techniques. Text content such as consumer reviews, email content etc. can be classified as favorable/unfavorable, spam/not-spam, etc. with a high success rate. News content too is known to affect human sentiment leading to sharp, short term price movements in stocks that follows a positive/negative news. The attached sample dataset may be used to train a machine learning model to classify news text and predict its influence on stock price, and subsequently to deduce buy/sell recommendations. A predicted downward price movement may also help institutions engaged in lombard lending (securities lending) employ proactive risk mitigation. The dataset contains news articles and the empirical stock price movements following the news publication da' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["News, Stocks, Stock, Portfolio, performance, Equities, Events"]} <|eot_id|>
 Here is the Description of a dataset. Description:'Text classification problems are quite successfully solved by current machine learning techniques. Text content such as consumer reviews, email content etc. can be classified as favorable/unfavorable, spam/not-spam, etc. with a high success rate. News content too is known to affect human sentiment leading to sharp, short term price movements in stocks that follows a positive/negative news. The attached sample dataset may be used to train a machine learning model to classify news text and predict its influence on stock price, and subsequently to deduce buy/sell recommendations. A predicted downward price movement may also help institutions engaged in lombard lending (securities lending) employ proactive risk mitigation. The dataset contains news articles and the empirical stock price movements following the news publication da' . Please return only a json list of at most 3 Keywords corresponding to the Description:.
Text classification problems are quite successfully solved by current machine learning techniques. Text content such as consumer reviews, email content etc. can be classified as favorable/unfavorable, spam/not-spam, etc. with a high success rate. News content too is known to affect human sentiment leading to sharp, short term price movements in stocks that follows a positive/negative news. The attached sample dataset may be used to train a machine learning model to classify news text and predict its influence on stock price, and subsequently to deduce buy/sell recommendations. A predicted downward price movement may also help institutions engaged in lombard lending (securities lending) employ proactive risk mitigation. The dataset contains news articles and the empirical stock price movements following the news publication da
News, Stocks, Stock, Portfolio, performance, Equities, Events