On the Statistical and Temporal Dynamics of Sentiment Analysis

Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at ..

Multimodal Document Analytics for Banking Process Automation

In response to growing FinTech competition and the need for improved operational efficiency, this research focuses on understanding the potential of advanced document analytics, particularly using multimodal models, in banking processes. We perform a comprehensive analysis of the diverse banking document landscape, highlighting the opportunities for efficiency gains through automation and advanced analytics techniques in the customer business. Building on the rapidly evolving field of natural language processing (NLP), we illustrate the potential of models such as LayoutXLM, a cross-lingual, multimodal, pre-trained model, for analyzing diverse documents in the banking sector. This model performs a text token classification on German company register extracts with an overall F1 score performance of around 80\%. Our empirical evidence confirms the critical role of layout information in improving model performance and further underscores the benefits of integrating image information. Interestingly, our study shows that over 75% F1 score can be achieved with only 30% of the training data, demonstrating the efficiency of LayoutXLM. Through addressing state-of-the-art document analysis frameworks, our study aims to enhance process efficiency and demonstrate the real-world applicability and benefits of multimodal models within banking.Comment: A Preprin

The car manufacturer (CM) and third party logistics provider (TPLP) relationship in the outbound delivery channel: A qualitative study of the Malaysian automotive industry

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.This research studies the relationship between car manufacturers (CM) and third party logistics providers (TPLP), also known as the logistics partnership, in the outbound delivery channel in the Malaysian automotive industry. It focuses specifically on the dyad perspective, and demonstrates that several critical success factors are required for a successful relationship between these two parties. Five such factors emanate from the operational dimension and eight from the relational dimension. The five operational factors are: logistics service performance, investment, information sharing, information technology and communication, and price of the logistics service; and the eight relational factors are: trust, commitment, power, conflict, dependency, co-operation, informal activity, and understanding. The study also reveals that five outcomes are identified that benefit both the CM and the TPLP as a result of the win-win situation accruing to both parties. These are: renewal of the contract, company profitability, improved logistics service performance, knowledge transfer, and company branding. Such benefits enhance the supply chain relationship, and knowledge of these advantages improves current TPLP theory by deepening the understanding of how logistics partnership can succeed. In order to obtain rich data concerning the CM-TPLP relationship, the researcher adopted a different methodology from that used by previous scholars, who have concentrated on quantitative techniques. In this study, multiple case studies (seven in total) in one industry, the automotive industry, in the non-western context of Malaysia, were conducted. Three main steps in the case study protocol were followed. The first involved a review of the literature pertaining to the themes that required further exploration, together with the development of the interview questions. In the second step, data were collected using semi-structured interviews, observations, document reviews, photographs and also archival records. Qualitative content analysis was used to analyse the qualitative data. The third stage involved exploring the data until it was found that nothing new was emerging from the interviews, and hence theoretical saturation had occurred. At this point the factors in question were confirmed, and the initial model revised. Additionally, confidentiality was maintained in all respects to protect the participating organisations and individuals. The findings contribute to the understanding of the CM-TPLP relationship which enhance supply chain relationship and TPLP theory, since they shed light on the operational and relational factors in one specific industry, from a dyadic perspective, and in a non-Western context, thereby adding new dimensions to the existing body of knowledge in this field. The findings benefit practitioners via the novel LPS (logistics partnership success) model generated by the researcher. This indicates the key contributory factors to the CM-TPLP relationship success. Moreover, the study may have the capacity to generalise to other culturally-similar environments.This study is supported by the Malaysian Government, MARA and UniKL

Assessing the Impacts of Crowdsourcing in Logistics and Supply Chain Operations

Crowdsourcing models, whereby firms start to delegate supply chain operations activities to a mass of actors in the marketplace, have grown drastically in recent years. 85% of the top global brands have reported to use crowdsourcing in the last ten year with top names such as Procter & Gamble, Unilever, and Nestle. These emergent business models, however, have remained unexplored in extant SCM literature. Drawing on various theoretical underpinnings, this dissertation aims to investigate and develop a holistic understanding of the importance and impacts of crowdsourcing in SCM from multiple perspectives. Three individual studies implementing a range of methodological approaches (archival data, netnography, and field and scenario-based experiments) are conducted to examine potential impacts of crowdsourcing in different supply chain processes from the customer’s, the crowdsourcing firm’s, and the supply chain partner’s perspectives. Essay 1 employs a mixed method approach to investigate “how, when, and why” crowdsourced delivery may affect customer satisfaction and behavioral intention in online retailing. Essay 2 uses a field experiment to address how the framing of motivation messages could enhance crowdsourced agents’ participation and performance level in crowdsourced inventory audit tasks. Lastly, Essay 3 explores the impact of crowdsourcing activities by the manufacturers on the relationship dynamics within the manufacturer-consumers-retailer triads

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty