# Using Social Media & Sentiment Analysis to Make Investment Decisions
## 2 Literature Review
### Sentiment Analysis
#### 2.3.3 Related Literature  
Based on the above findings, this paper has found that the LRA, SVM, and NN algorithms all perform well for SA and will consider them when implementing the prototype. Finally, to complete this literature review, a higher-level investigation into other related research is undertaken to help find any information that could be of use to support our approach. Recent research has shown that it could be possible to use SA to help make investment decisions by extracting and analyzing emotions from users. [PERSON] et al. (2015) agrees with this and suggests that SA is an effective tool to model capital markets and cryptocurrencies. This is further analyzed in the work of [PERSON] et al. [PERSON] et al. (2016), which looked at stock trends from over the last 2-3 decades and found that SA can be used to help make investment decisions. Renault in Renault (2016), which used SA to compare the returns of selected stocks and SM sentiment,found that certain algorithms were able to show a correlation between the two data sources. However, they also found that the sentiment was not able to provide insight into predicted stock returns. Overall, although the work in this field of study has found positive results, generally the conglomerate view is that more work needs to be undertaken to further prove this [10; 11]. Earlier research [5] proposed that the performance of the DJIA could be predicted using sentiment gathered from Twitter to help make investment decisions. The sentiment of these tweets was predicted using tools such as Opinion Finder, a SA tool developed in a collaborative process by different universities. The findings showed accuracy results of up to 86.7% for predicting the daily ups and downs of the DJIA. However, the lack of test samples in this research devalues the findings. The work of [PERSON] [27] proposed using three types of features extracted from social media/stocks data to make Chinese stock market predictions: Sentiment Features (SF), Stock Specific Features (SSF) and Stock Relatedness Features (SRF). The SFs were classified using naive Bayes and proved to be a useful measurement to make predictions, and thus help make investment decisions. This is presented in Figure 4 which clearly shows that the accuracy results of the SF's + SSF's, is higher than that of just SSF and SSF + SRF. However, the accuracy value and the area under a curve value are both under 58%, which, devalues the findings. Other work undertaken by [28] addresses the issue of high-speed streaming data outputted by SM platforms and the challenges faced in analyzing the sentiment of it in real time. It goes on to explain how this vast SM data generally consists of short text, which can make it harder to identify the related topics, and the existing topic-based NLP models struggle processing this information quickly. Their work proposes a deep learning system that works at sentence level to extract topics using an online latent semantic indexing and a regularization constraint to efficiently extract the topics from SM content, and then uses a long, short term memory network and a topic level attention mechanism to perform SA. The system designed uses an online learning technique and works at sentence level to identify topics and process short text data in a serial fashion. This enables large datasets to be used and it makes the system scalable. The paper found that the system was able to process larger quantities of data much faster than the existing models and was able to achieve over 80% accuracy in certain topics. These findings are useful, as the prototype created in our work also needs to consider speed and scalability when mining data from SM. Another recent paper [15], analyzed a variety of related sources that used SA techniques on SM data to make election predictions. One finding was that Twitter's Streaming API was the by far the most used to extract data from SM, compared to less popular options such as the Twitter Search API.