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Predicting Stock Returns by Automatically Analyzing Company News Announcements Bella Duroc November 1, 2015Abstract We study the impact of company news announcements on stock prices and show that
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How to fill out news versus sentiment predicting

01
Step 1: Collect labeled data- gather a dataset of news articles along with their corresponding sentiment labels.
02
Step 2: Preprocess the data- clean the text data by removing any unnecessary characters, lowercasing all text, and tokenizing the articles into words.
03
Step 3: Vectorize the data- convert the words into numerical representations using techniques like bag-of-words or word embeddings.
04
Step 4: Split the data- divide the dataset into train and test sets to evaluate the performance of the predictive model.
05
Step 5: Train a model- choose a machine learning algorithm like Logistic Regression, Naive Bayes, or LSTM, and train it on the labeled data.
06
Step 6: Evaluate the model- assess the model's performance using metrics like accuracy, precision, recall, and F1-score.
07
Step 7: Make predictions- use the trained model to predict sentiment for new, unseen news articles.
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Step 8: Fine-tune and iterate- adjust the model parameters, try different algorithms, or explore other preprocessing techniques to improve the prediction accuracy.

Who needs news versus sentiment predicting?

01
News outlets- to analyze public sentiment towards their articles or news topics
02
Marketing companies- to understand customer reactions to their campaigns or advertisements
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Financial institutions- to predict stock market sentiment and make informed investment decisions
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Social media platforms- to identify and moderate content with potentially harmful or offensive sentiment
05
Researchers- to study the correlation between news sentiment and real-world events or public opinions
06
General public- to obtain insights into the sentiment surrounding certain news topics or events
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News predicting is forecasting future news events while sentiment predicting is predicting the emotional tone behind those news events.
Individuals or organizations involved in the news industry or sentiment analysis are usually required to file news versus sentiment predicting.
News and sentiment predicting can be filled out by collecting data, analyzing trends, and making predictions based on the information gathered.
The purpose of news versus sentiment predicting is to anticipate future news events and understand the emotional impact of those events.
Information reported on news versus sentiment predicting may include data on news topics, sentiment analysis results, and predicted outcomes.
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