Extracting business performance signals from Twitter news

Social media and social networks underpin a revolution in communication between people, with the particular feature that much of that communication is open to all. This provides a massive pool of data that can be exploited by researchers for a wide variety of different applications. Data from Twitter is of particular interest in this sense, given its large global usage levels, and the availability of APIs and other tools that enable easy access to the publicly available stream of tweets. Owing to the wide public penetration of Twitter, many businesses make use of it to share their latest news, effectively using Twitter as a gateway to connect to end-users, consumers and/or investors. In this thesis, we focus on the potential for extracting information from Twitter that is relevant to the financial and competitiveness status of a business. We consider a collection of well-regarded Twitter accounts that are known for communicating recent business news, and we investigate the automated analysis of the stream of tweets from these sources, with a view to learning business-relevant information about specific companies. A key aspect of our approach is the idea of extracting specific areas of business performance: we explore three such areas: productivity, competitiveness, and industrial risk. We propose a two-step model which first classifies a tweet into one of these areas, and then assigns a sentiment value (on a positive/negative scale). The resulting sentiment values across specific aspects represent novel business indicators that could add significant value to the toolset used by business analysts. Our experiments are based on a new manually pre-classified data set (available from a URL provided). Additionally, we propose n-grams made from non-contiguous words as a novel feature to enhance performance in this context. Experiments involving a range of feature selection methods show that these new features provide valuable benefits in comparison with standard n-gram features. We also interduce the concept of an extra layer added to the primary classifier, with the role of filtering out noisy tweets before they enter the system. We use a One-Class SVM for this purpose. Broadly, we show that the methods developed in this thesis achieve promising results in both topic and sentiment classification in the business performance context, suggesting that twitter can indeed be a useful source of signals related to different aspects of business performance. We also find that our system can provide valuable insight into unseen test data. However, more research is needed to be able to extract robust signals for industrial risk, and there seems to be a considerable promise for further development

Evaluating the impact of social-media on sales forecasting: a quantitative study of worlds biggest brands using Twitter, Facebook and Google Trends

In the world of digital communication, data from online sources such as social networks might provide additional information about changing consumer interest and significantly improve the accuracy of forecasting models. In this thesis I investigate whether information from Twitter, Facebook and Google Trends have the ability to improve daily sales forecasts for companies with respect to the forecasts from transactional sales data only. My original contribution to this domain, exposed in the present thesis, consists in the following main steps: 1. Data collection. I collected Twitter, Facebook and Google Trends data for the period May 2013 May 2015 for 75 brands. Historical transactional sales data was supplied by Certona Corporation. 2. Sentiment analysis. I introduced a new sentiment classification approach based on combining the two standard techniques (lexicon-based and machine learning based). The proposed method outperforms the state-of-the-art approach by 7% in F-score. 3. Identification and classification of events. I proposed a framework for events detection and a robust method for clustering Twitter events into different types based on the shape of the Twitter volume and sentiment peaks. This approach allows to capture the varying dynamics of information propagation through the social network. I provide empirical evidence that it is possible to identify types of Twitter events that have significant power to predict spikes in sales. 4. Forecasting next day sales. I explored linear, non-linear and cointegrating relationships between sales and social-media variables for 18 brands and showed that social-media variables can improve daily sales forecasts for the majority of brands by capturing factors, such as consumer sentiment and brand perception. Moreover, I identified that social-media data without sales information, can be used to predict sales direction with the accuracy of 63%. The experts from the industry consider the results obtained in this thesis to be valuable and useful for decision making and for making strategic planning for the future