Predictive Analysis on Twitter: Techniques and Applications

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive experimentation and analysis for insights have been carried out using Twitter data in various domains such as healthcare, public health, politics, social sciences, and demographics. In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data. Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking actions, and relate a few success stories

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

There is a general consensus of the good sensing and novelty character- istics of Twitter as an information media for the complex fi nancial market. This paper investigates the permeability of Twitter sphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to fi nancial-specifi c events and establishes Twitter as a relevant feeder for taking decisions regarding the fi nancial market and event fraudulent activities in that market. However, the provenance of contributions, their diferent levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specifi c financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange (LSE), to create the UK's Leading Food Business. The experiment attempts to answer two research questions which aim to characterize the features of Twitter permeability to the fi nancial market. The experimental results con rm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specifi c fi nancial forum, it is permeable to financial events

Predikce abnormálních výnosů bank pomocí textové analýzy výročních zpráv - Přístup založený na neuronových sítích

This paper aims to extract both sentiment and bag-of-words information from the annual reports of U.S. banks. The sentiment analysis is based on two commonly used finance-specific dictionaries, while the bag-of-words are selected according to their tf-idf. We combine these features with financial indicators to predict abnormal bank stock returns using a neural network with dropout regularization and rectified linear units. We show that this method outperforms other machine learning algorithms (Naïve Bayes, Support Vector Machine, C4.5 decision tree, and k-nearest neighbour classifier) in predicting positive/negative abnormal stock returns. Thus, this neural network seems to be well suited for text classification tasks working with sparse high-dimensional data. We also show that the quality of the prediction significantly increased when using the combination of financial indicators and bigrams and trigrams, respectively.Tento článek si klade za cíl získávat z výročních zpráv amerických bank jak sentiment, tak informaci ve formě bag-of-words. Analýza sentimentu je založena na dvou běžně používaných finančních slovnících, zatímco bag-of-words jsou vybírány v závislosti na tf-idf. Kombinujeme tyto atributy společně s finančními ukazateli s cílem predikce abnormálních výnosů bank pomocí neuronové sítě s regularizací a rektifikovanými lineárními jednotkami. Ukázali jsme, že tato metoda překonává ostatní algoritmy strojového učení (Naivní Bayes, podpůrné vektorové stroje, rozhodovací strom C4.5 a klasifikátor k-nejbližšího souseda) v predikci pozitivních/negativních abnormálních výnosů. Proto se tato neuronová síť zdá být vyhovující pro úlohy klasifikace textu, kde se pracuje s řídkými vysoce dimenzionálními daty. Také ukazujeme, že se kvalita predikce významně zvýšila při použití kombinace finančních ukazatelů a bigramů (trigramů)