# Sentiment Analysis of Twitter Data for Predicting Stock Market Movements

arXiv: https://arxiv.org/abs/1610.09225 · HTML: https://mlsanity.com/paper/1610.09225/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2016.

## Built on by (most cited first, 30 of 34)

- [Stock Movement Prediction with Financial News using Contextualized Embedding from BERT](https://mlsanity.com/paper/2107.08721/) (2021)
- [Can LLMs Replace Economic Choice Prediction Labs? The Case of Language-based Persuasion Games](https://mlsanity.com/paper/2401.17435/) (2024)
- [Text Mining of Stocktwits Data for Predicting Stock Prices](https://mlsanity.com/paper/2103.16388/) (2021)
- [PreBit -- A multimodal model with Twitter FinBERT embeddings for extreme price movement prediction of Bitcoin](https://mlsanity.com/paper/2206.00648/) (2022)
- [FinLLMs: A Framework for Financial Reasoning Dataset Generation with Large Language Models](https://mlsanity.com/paper/2401.10744/) (2024)
- [Using LLMs to Establish Implicit User Sentiment of Software Desirability](https://mlsanity.com/paper/2408.01527/) (2024)
- [Using Twitter Attribute Information to Predict Stock Prices](https://mlsanity.com/paper/2105.01402/) (2021)
- [#StayHome or #Marathon? Social Media Enhanced Pandemic Surveillance on Spatial-temporal Dynamic Graphs](https://mlsanity.com/paper/2108.03670/) (2021)
- [Sentiment and structure in word co-occurrence networks on Twitter](https://mlsanity.com/paper/2110.00587/) (2021)
- [Forecasting Financial Market Structure from Network Features using Machine Learning](https://mlsanity.com/paper/2110.11751/) (2021)
- [Multi-modal Attention Network for Stock Movements Prediction](https://mlsanity.com/paper/2112.13593/) (2021)
- [Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets](https://mlsanity.com/paper/2204.05783/) (2022)
- [Evaluating Impact of Social Media Posts by Executives on Stock Prices](https://mlsanity.com/paper/2211.01287/) (2022)
- [Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review](https://mlsanity.com/paper/2305.14842/) (2023)
- [Impact of the COVID-19 outbreak on Italy's country reputation and stock market performance: a sentiment analysis approach](https://mlsanity.com/paper/2103.13871/) (2021)
- [Sentiment Analysis for Measuring Hope and Fear from Reddit Posts During the 2022 Russo-Ukrainian Conflict](https://mlsanity.com/paper/2301.08347/) (2023)
- [Artificial intelligence prediction of stock prices using social media](https://mlsanity.com/paper/2101.08986/) (2021)
- [A Comprehensive Review on Summarizing Financial News Using Deep Learning](https://mlsanity.com/paper/2109.10118/) (2021)
- [Sentiment analysis on electricity twitter posts](https://mlsanity.com/paper/2206.05042/) (2022)
- [Interdisciplinary Discovery of Nanomaterials Based on Convolutional Neural Networks](https://mlsanity.com/paper/2212.02805/) (2022)
- [Using Twitter Data to Determine Hurricane Category: An Experiment](https://mlsanity.com/paper/2308.05866/) (2023)
- [Predicting Financial Market Trends using Time Series Analysis and Natural Language Processing](https://mlsanity.com/paper/2309.00136/) (2023)
- [Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction](https://mlsanity.com/paper/2309.05608/) (2023)
- [Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT](https://mlsanity.com/paper/2309.11979/) (2023)
- [Analyzing Trendy Twitter Hashtags in the 2022 French Election](https://mlsanity.com/paper/2310.07576/) (2023)
- [Sentiment Analysis in Digital Spaces: An Overview of Reviews](https://mlsanity.com/paper/2310.19687/) (2023)
- [Twitter Permeability to financial events: an experiment towards a model for sensing irregularities](https://mlsanity.com/paper/2312.11530/) (2023)
- [The irruption of cryptocurrencies into Twitter cashtags: a classifying solution](https://mlsanity.com/paper/2312.11531/) (2023)
- [Sentiment-driven prediction of financial returns: a Bayesian-enhanced FinBERT approach](https://mlsanity.com/paper/2403.04427/) (2024)
- [Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models](https://mlsanity.com/paper/2409.15988/) (2024)

## Abstract (arXiv)

Predicting stock market movements is a well-known problem of interest. Now-a-days social media is perfectly representing the public sentiment and opinion about current events. Especially, twitter has attracted a lot of attention from researchers for studying the public sentiments. Stock market prediction on the basis of public sentiments expressed on twitter has been an intriguing field of research. Previous studies have concluded that the aggregate public mood collected from twitter may well be correlated with Dow Jones Industrial Average Index (DJIA). The thesis of this work is to observe how well the changes in stock prices of a company, the rises and falls, are correlated with the public opinions being expressed in tweets about that company. Understanding author's opinion from a piece of text is the objective of sentiment analysis. The present paper have employed two different textual representations, Word2vec and N-gram, for analyzing the public sentiments in tweets. In this paper, we have applied sentiment analysis and supervised machine learning principles to the tweets extracted from twitter and analyze the correlation between stock market movements of a company and sentiments in tweets. In an elaborate way, positive news and tweets in social media about a company would definitely encourage people to invest in the stocks of that company and as a result the stock price of that company would increase. At the end of the paper, it is shown that a strong correlation exists between the rise and falls in stock prices with the public sentiments in tweets.