Critical review of text mining and sentiment analysis for stock market prediction

The paper is aimed at a critical review of the literature dealing with text mining and sentiment analysis for stock market prediction. The aim of this work is to create a critical review of the literature, especially with regard to the latest findings of research articles in the selected topic strictly focused on stock markets represented by stock indices or stock titles. This requires examining and critically analyzing the methods used in the analysis of sentiment from textual data, with special regard to the possibility of generalization and transferability of research results. For this reason, an analytical approach is also used in working with the literature and a critical approach in its organization, especially for completeness, coherence, and consistency. Based on the selected criteria, 260 articles corresponding to the subject area are selected from the world databases of Web of Science and Scopus. These studies are graphically captured through bibliometric analysis. Subsequently, the selection of articles was narrowed to 49. The outputs are synthesized and the main findings and limits of the current state of research are highlighted with possible future directions of subsequent research

Recent Advances in Stock Market Prediction Using Text Mining: A Survey

Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study

"Our city will be the first to hold both summer and winter olympics" : a comparative analysis of how media coverage and public opinion were framed on social media in the lead up to the Beijing 2022 winter Olympic games

Beijing is the first city to host both the Summer and Winter Olympic Games. Mega sporting events such as the Olympic Games, which attract mass audiences, benefit greatly from social media. This article examines how the news coverage and public opinion about the Beijing 2022 were articulated on social media in the lead up to the Beijing 2022. We employed computational content analysis to examine 9,439 individual posts and 450 official media posts that appeared before the Beijing 2022 Olympics. We also used ROSTCM6 to investigate the sentiment of official media and public opinion toward Beijing 2022. The results of this study reveal that members of public are more inclined to highlight certain aspects of Beijing 2022 based on their individual perspectives. Official media, whose work generally aligns with the government's interests. Through a sentiment analysis of these posts, we found strongly positive attitudes concerning Beijing 2022 among the Chinese public and the media. Our results provide ample evidence of an overall relative convergence of positions between public opinion and news coverage about the Beijing 2022, despite their divergences. This study indicates that social media presents itself as a space for broader public statements, and empowers ordinary people to discuss China's social issues of concern. Meanwhile, official media represents the government's position, strategically framing Beijing 2022 as a landmark event in the new era of China

Statistical data mining for Sina Weibo, a Chinese micro-blog: sentiment modelling and randomness reduction for topic modelling

Before the arrival of modern information and communication technology, it was not easy to capture people’s thoughts and sentiments; however, the development of statistical data mining techniques and the prevalence of mass social media provide opportunities to capture those trends. Among all types of social media, micro-blogs make use of the word limit of 140 characters to force users to get straight to thepoint, thus making the posts brief but content-rich resources for investigation. The data mining object of this thesis is Weibo, the most popular Chinese micro-blog. In the first part of the thesis, we attempt to perform various exploratory data mining on Weibo. After the literature review of micro-blogs, the initial steps of data collection and data pre-processing are introduced. This is followed by analysis of the time of the posts, analysis between intensity of the post and share price, term frequency and cluster analysis. Secondly, we conduct time series modelling on the sentiment of Weibo posts. Considering the properties of Weibo sentiment, we mainly adopt the framework of ARMA mean with GARCH type conditional variance to fit the patterns. Other distinct models are also considered for negative sentiment for its complexity. Model selection and validation are introduced to verify the fitted models. Thirdly, Latent Dirichlet Allocation (LDA) is explained in depth as a way to discover topics from large sets of textual data. The major contribution is creating a Randomness Reduction Algorithm applied to post-process the output of topic models, filtering out the insignificant topics and utilising topic distributions to find out the most persistent topics. At the end of this chapter, evidence of the effectiveness of the Randomness Reduction is presented from empirical studies. The topic classification and evolution is also unveiled