Datasets
Standard Dataset
STOCK MARKET SENTIMENT ANALYSIS
- Citation Author(s):
- Submitted by:
- Apurba Nandi
- Last updated:
- Thu, 04/03/2025 - 06:15
- DOI:
- 10.21227/wah7-bm15
- License:
18 Views- Categories:
- Keywords:
Abstract
This study explores the relationship between social media sentiment and stock market movements using a dataset of tweets related to various publicly traded companies. The dataset comprises time-stamped tweets containing company-specific information, stock ticker symbols, and company names. By leveraging natural language processing (NLP) techniques, we analyze the sentiment of tweets to determine their impact on stock price fluctuations. This research aims to develop predictive models that incorporate tweet sentiment and frequency as features to forecast stock price movements. The insights from this analysis could assist investors in making data-driven decisions and offer valuable input for algorithmic trading strategies.
To use the stock tweets dataset, first, ensure you have Python 3.x installed along with libraries like pandas, numpy, nltk, sklearn, matplotlib, and vaderSentiment. Load the dataset using pandas and convert the "Date" column to datetime format. Clean the "Tweet" column by removing URLs, mentions, hashtags, special characters, and stopwords using nltk. Perform sentiment analysis using VADER or a transformer model (e.g., BERT) by applying sentiment scores (positive, negative, neutral) to each tweet. Analyze sentiment trends over time and visualize sentiment distributions for each stock using plotting tools like matplotlib or seaborn. Finally, use the cleaned data and sentiment scores as input features for predictive models to forecast stock price movements or market trends.
More from this Author
