This GitHub repository examining the impact of Twitter trends and influencers on stock market fluctuations. Leveraging sentiment analysis, the project analyzes tweets from corporate accounts and key influencers. The objective is to understand how social media sentiment influences market prices and help investors make informed decisions.

Due to the development of social networks in the last two decades and the implications of these in our daily, it has made it possible for companies or market makers to affect the stock market through their news and postings.

Does that make sense?

This project deals with assessing and analyzing the effect of social network information on investors and should help him make decisions and manage risk more effectively alongside other technical tools, fundamental analysis, and so on.

A study from the University of Maryland found that the content of tweets made by corporations on Twitter affected their share price. Therefore, I focused on Twitter's trends and influencers in the context of the stock market. Identify the sentiment of tweets based on lexicon models used for sentiment analysis (also known as opinion mining or emotion AI), a sub-field of NLP that tries to identify and extract opinions within a given text across blogs, reviews, and social media, forums, news, etc.

This analysis will be exploited to determine if there is any influence and how much influence it has on the market prices of the top companies. In addition, examine influencers' (public opinion leader) tweets and activities based on engagement determined by calculating retweets, amount of comments, likes, and so forth.

Ewelina Lacka, D. Eric Boyd, Gbenga Ibikunle, and P.K. Kannan (2021), “Measuring the Real-Time Stock Market Impact of Firm-Generated Content”, Journal of Marketing, Robert H. Smith School of Business at the University of Maryland

• Yehuda Finkelshtein
• Guy Cohen
• Daniel Biton

• Tweets dataset

The key scientific question is:

How does Twitter affect the stock market?

Tagging and Filtering millions of tweets by their sentiment (negative, positive or neutral) refers to companies and checking the correlation by the time plot of the stock price to the sentiment analysis.

We will use the VADER-Sentiment-Analysis to classify the given tweets to their sentiment segments. As we advance, we will compare how the company's price varies after the post.