# Stock Market Prediction Using Microblogging Sentiment Analysis and Machine Learning
## 2 Background
### Sentiment Analysis Applications
The system contained three modules: Microblog Filter (MF), Sentiment Analysis (SA), and Stock Prediction (SP). Financial microblog data were obtained by incorporating Latent Dirichlet Allocation (LDA). The SA module first builds a financial vocabulary, then obtains sentiment from the MF module. For predicting SH000001 price movements, the SP module presents a user-group model that adjusts the relevance of various people and blends it with previous stock data. This approach was proven to be successful, since it was tested on 6.1 million microblogs. SA on microblogs has recently become a prominent business analytics technique investigating how this sentiment can affect SMP. In [20], vector autoregression was performed on a data collection of four years. The dataset contained 18 million microblog messages at a daily and hourly frequency. The findings revealed that microblog sentiment affects stock returns in a statistically and economically meaningful way. Market fluctuation influenced microblog sentiment and stock returns affected negative sentiment more than positive sentiment. Another study presented a microblogging data model that estimates stock market returns, volatility, and trade volume [21]. Indicators from microblogs (a large Twitter dataset) and survey indices (AAII and II, USMC and Sentix) were used to aggregate these indicators in daily resolution. A Kalman Filter (KF) was used to merge microblog and survey sources. Findings showed that Twitter sentiment and tweeting volume were useful for projecting S&P 500 returns. Additionally, Twitter and KF sentiment indicators helped forecast survey sentiment indicators. Thus, it is concluded that microblogging data may be used to anticipate stock market activity and provide a viable alternative to traditional survey solutions. Time-series analysis is a prominent tool for predicting stock prices. However, relying solely on stock index series results in poor forecasting performance. As a supplement, SA on online textual data of the stock market may provide a lot of relevant information, which can act as an extra indicator. A novel technique is proposed for integrating such indicators and stock index time-series [22]. A text processing approach was suggested to produce a weighted sentiment time-series. The data were crawled from financial micro-blogs from prominent Chinese websites. Each microblog was segmented and preprocessed, and the sentiment values were determined on a daily basis. The proposed time-series model with weighted sentiment series was validated by predicting the future value of the Shanghai Composite Index (SSECI). The authors of [23] investigated a possible association between the Chinese stock market and Chinese microblogs. At first, C-POMS (Chinese Profile of Mood States) was proposed for SA on microblog data. Then, the Granger causality test validated the existence of association between C-POMS and stock prices. Utilizing different prediction models for SMP showed that Support Vector Machine (SVM) outperforms Probabilistic NeuralNetworks (PNN) in terms of accuracy. Adding a dimension of C-POMS to the input data improved accuracy to 66.66%. Utilizing two dimensions to the input data resulted in 71.42% accuracy, which is nearly 20% greater than just using historical data.