# Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in Spark

arXiv: https://arxiv.org/abs/2201.12283 · HTML: https://mlsanity.com/paper/2201.12283/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [Stock trend prediction using news sentiment analysis](https://mlsanity.com/paper/1607.01958/) (2016)

## Abstract (arXiv)

Predicting the stock market trend has always been challenging since its movement is affected by many factors. Here, we approach the future trend prediction problem as a machine learning classification problem by creating tomorrow_trend feature as our label to be predicted. Different features are given to help the machine learning model predict the label of a given day; whether it is an uptrend or downtrend, those features are technical indicators generated from the stock's price history. In addition, as financial news plays a vital role in changing the investor's behavior, the overall sentiment score on a given day is created from all news released on that day and added to the model as another feature. Three different machine learning models are tested in Spark (big-data computing platform), Logistic Regression, Random Forest, and Gradient Boosting Machine. Random Forest was the best performing model with a 63.58% test accuracy.