# Using Social Media & Sentiment Analysis to Make Investment Decisions
## 2 Literature Review
### Social Media  
Now this paper has identified the strong relationship between public sentiment and stock market performance, an investigation into the ways of harvesting this sentiment for SA will be discussed. Additionally, this sentiment must be text based, related to financial data, and easy to access. Undoubtedly, one of the most effective ways to gather this data would be using SM. This is exemplified in [10], which explains that SM is a place where investors leave their thoughts and ideas, and it can be obtained by anyone who has access to the internet. However, there are many SM platforms to choose from, and it's vital that the one selected for our approach meets the criteria mentioned above. The Twitter API is a common tool for extracting public sentiment and is used in almost every source referenced in this literature review. It enables development access to Twitter, a popular SM platform that allows users to share their opinions, most famously in a text-based format limited to 280 characters (as of October 2021), which is a format that ties perfectly into NLP [12]. [PERSON] [3], who investigated price predictions of cryptocurrencies using sentiment from Twitter and Google Trends, states, \"Twitter is one of the most widely used social media platforms and reflects diverse perspectives from users worldwide\" [3]. An older study by [PERSON] et al. [5] found that the value of the Dow Jones Industrial Average (DJIA) could be predicted more accurately using data from large-scale twitter feeds. However, the main weakness of this study is that it was conducted over a decade ago and there have been many political, technological, and economical changes since then: most relevantly, the huge increase of active SM users. More recently, [PERSON] in [13] found that certain stocks could skyrocket after positive SM content about that company were posted by influential individuals. In this article, the individual was [PERSON] and after tweeting, \"I kinda love Etsy\", the share price of the e-commerce company increased by 9%. CD Projekt shares also soared by more than 12% after Mr. [PERSON] tweeted \"With Cyberpunk, even the hoffixes literally have hoffixes, but... great game.\" These findings are like that of [6], who also acknowledges that influential twitter users such as CEO's, politicians and journalists can have a greater impact on individual stocks. Based on these findings, it appears that analyzing sentiment from more influential individuals could be an effective way to make investment decisions. This is well presented in Figure 3, which clearly illustrates the decrease of Bitcoin's value after a negative tweet about Bitcoin was posted by Mr. [PERSON]. However, much of this work only focuses on a very limited group of highly influential individuals, and there is a lack of evidence to show that the same can be said for other less influential SM users. Additionally, the goal of this work is to analyze the general sentiment of the public and will not focus purely on highly influential individuals. [PERSON] in [1], who analyzed the sentiment of tweets to make stock market predictions, found that posts with higher engagement is a good indicator on the sentiment agreeability of others. More specifically, a post with a higher number of likes, comments, and reactions, is a better post to analyze than a post with less, as it can give insight into the collective sentiment of multiple users. This is acknowledged in the work of [PERSON] [14], which factored Twitter likes, tweets and retweets--using a weighting factor formula--into a tweet sentiment system they designed, gaining insight into the collective social distancing beliefs of individuals throughout the COVID-19 pandemic. Although this study has acknowledged the work undertaken in [5] is dated in certain aspects, it also identifies relevant findings as well: especially in its discussion about extracting sentiment from SM. This was achieved by choosing tweets that contained phrases like \"I feel\" or \"I'm feeling\".