# Using Social Media & Sentiment Analysis to Make Investment Decisions
## 1 Introduction  
\"Beating the market\" has always captivated the minds of economists, investors, and researchers alike. There is endless discussion and investigation to try and better understand how to profit from it. One related area of interest considers using public sentiment from SM. This is exemplified by [PERSON] [1] who states, \"leveraging social media to get the fastest news impacting stock prices isn't a theory anymore, it is a reality\". Over 2.3 billion active users were recorded using Facebook in 2021 [2], and recent studies have proven that the sentiment data obtained from this vast pool of people can be utilized to make stock market decisions, and potentially even predictions [3]. This relationship between stock market performance and public sentiment can be seen clearly in certain instances. Most notably when popular individuals share their sentiment about certain stocks and directly impact the performance of that stock [4]. However, although there is continued research into this topic, much of the presented work includes mixed results [5], and there is still an opportunity for further investigation.  
One of the main challenges faced in this field of work is being able to create a system which can extract and calculate the sentiment of vast quantities of SM data efficiently. Currently, the best way to achieve this is by using computational power and Machine Learning. Machine Learning is becoming ubiquitous in our lives and can be found in speech recognition technologies, image classification tools, autonomous driving, and many other areas. The specific area of Machine Learning that can be used to analyze the sentiment from SM efficiently is Sentiment Analysis (SA), a type of data intelligence used to identify the emotion behind text [6]. It can be implemented using Natural Language Processing (NLP), and to work effectively it needs to be configured specifically for its purpose, soan understanding of the right datasets and types of algorithms to use is fundamental. Therefore, determining what type of SA algorithm to use for predicting SM sentiment is vital in our goal of helping users make investment decisions.  
The first aim of this work is to provide insight into the field of study and outline existing research and technologies. Firstly, it will look to understand the relationship between public sentiment and stock market performance, then the sentiment from SM, and finally the topic of SA itself. This will also include a technical review aspect, where it will present existing approaches for creating SA models, extracting SM data, and evaluating results.  
The second aim of this work is to show how it is possible to utilize public SM sentiment to help users make investment decisions. We will show this by designing and implementing a prototype that will look to track the stock market performance of selected S&P500 stocks, using the sentiment data that it will obtain from SM. Furthermore, a novel approach to factor SM metrics into the sentiment score will be presented, in which a post like weighting is factored into the SA algorithm sentiment score. The goal of this is to try and measure the collective sentiment of the data effectively.  
The rest of the paper is structured as follows: In Section 2 an in-depth literature review is provided. Section 3 builds upon Section 2 and the proposed methodology and implementation of a prototype are presented. Section 4 discusses the results and the evaluation of the proposed approach and finally Section 5 concludes our work.