# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 2 Background
### Sentiment Analysis Applications
Three attributes were chosen for the model: date, stock price choices, and sentiment. The accuracy of the training and test models was 75.22% and 76.68%, respectively. The authors claim that the findings may be improved by expanding the volume of the data collection [14]. The relationship between StockTwits emotion and stock price fluctuations was investigated for five businesses, including Microsoft, Apple, General Electric, Target, and Amazon. Stock market and StockTwits data were gathered from 1 January 2019 to 30 September 2019. SA was conducted by utilizing three classifiers (LR, NB and SVM) and five featurization algorithms (bigram, LSA, trigram, bag of words and TF-IDF). This implementation was created to anticipate stock price changes for a day using both financial data and collected feelings from the preceding five days. According to the performance evaluation, accuracy varied from 62.3% to 65.6%, with Apple and Amazon exhibiting the greatest results [15]. Another study focused on the high stock market capitalization and a distinctive SM ecosystem to investigate the link between stock price movements and SM sentiment in China [16]. Collected data varied in terms of activity, post length, and association with stock market performance. Stock market chatroom users tended to write more but shorter blogs. Trading hours and volume were far more closely linked to activity. Multiple ML models were incorporated to identify post sentiment in chatrooms, and produced results comparable to the current state of the art. Because of the substantial connection and Granger causality between chatroom post sentiment and stock price movement, post sentiment may be utilized to enhance stock price prediction over only analyzing historical trade data. Furthermore, a trading strategy was designed based on the prediction of trading indicators. This buy-and-hold strategy was tested for seven months and yielded a 19.54% total return, versus a loss of \(-\)25.26%. Authors in [17] consider three distinct equities' closing prices and daily returns to examine how SM data may predict Tehran Stock Exchange (TSE) factors. Three months of StockTwits were gathered. A lexicon-based and learning-based strategy was proposed to extract data from online discussions. Additionally, a custom sentiment lexicon was created, since existing Persian lexicons are not suitable for SA. Novel predictor models utilizing multi-regression analysis were proposed after creating and computing daily sentiment indices based on comments. The number of comments and the users' trustworthiness were also considered in the analysis. Findings show that the predictability of TSE equities variesby their features. For estimating the daily return, it is shown that both comment volume and mood may be beneficial, and the three stocks' trust coefficients behave differently. Stock price prediction is a topic with both potential and obstacles. There are numerous recent stock price prediction systems, yet their accuracy is still far from sufficient. The authors of [18] propose S_I_LSTM, a stock price prediction approach that includes numerous data sources as well as investor sentiment. First, data from different Internet sources are preprocessed. Non-traditional data sources such as stock postings and financial news are also included. Then, for non-traditional data, a Convolutional Neural Network (CNN) SA approach is applied to generate the investor sentiment index. As a last step, a Long Short-Term Memory (LSTM) network is utilized to anticipate the Shanghai A-share market. It incorporates sentiment indexes, technical indicators, and historical transaction data. With a Mean Absolute Error (MAE) value of 2.38, this approach outperforms some existing methods when compared to actual datasets from five listed businesses. Another research study suggests a SMP system based on financial microblog sentiment from Sina Weibo [19]. SA was performed on microblogs to anticipate stock market movements using historical data from the Shanghai Composite Index (SH000001).