Semantic-aware Retrieval Standards based on Dirichlet Compound Model to Rank Notifications by Level of Urgency

There is a growing number of notifications generated from a wide range of sources. However, to our knowledge, there is no well-known generalizable standard for detecting the most urgent notifications. Establishing reusable standards is crucial for applications in which the recommendation (notification) is critical due to the level of urgency and sensitivity (e.g. medical domain). To tackle this problem, this thesis aims to establish Information Retrieval (IR) standards for notification (recommendation) task by taking semantic dimensions (terms, opinions, concepts and user interaction) into consideration. The technical research contributions of this thesis include but not limited to the development of a semantic IR framework based on Dirichlet Compound Model (DCM); namely FDCM, extending FDCM to the recommendation scenario (RFDCM) and proposing novel opinion-aware ranking models. Transparency, explainability and generalizability are some benefits that the use of a mathematically well-defined solution such as DCM offers. The FDCM framework is based on a robust aggregation parameter which effectively combines the semantic retrieval scores using Query Performance Predictors (QPPs). Our experimental results confirm the effectiveness of such approach in recommendation systems and semantic retrieval. One of the main findings of this thesis is that the concept-based extension (term-only + concept-only) of FDCM consistently outperformed both terms-only and concept-only baselines concerning biomedical data. Moreover, we show that semantic IR is beneficial for collaborative filtering and therefore it could help data scientists to develop hybrid and consolidated IR systems comprising content-based and collaborative filtering aspects of recommendation

A Systematic Literature Review on Cyberbullying in Social Media: Taxonomy, Detection Approaches, Datasets, And Future Research Directions

In the area of Natural Language Processing, sentiment analysis, also called opinion mining, aims to extract human thoughts, beliefs, and perceptions from unstructured texts. In the light of social media's rapid growth and the influx of individual comments, reviews and feedback, it has evolved as an attractive, challenging research area. It is one of the most common problems in social media to find toxic textual content.  Anonymity and concealment of identity are common on the Internet for people coming from a wide range of diversity of cultures and beliefs. Having freedom of speech, anonymity, and inadequate social media regulations make cyber toxic environment and cyberbullying significant issues, which require a system of automatic detection and prevention. As far as this is concerned, diverse research is taking place based on different approaches and languages, but a comprehensive analysis to examine them from all angles is lacking. This systematic literature review is therefore conducted with the aim of surveying the research and studies done to date on classification of  cyberbullying based in textual modality by the research community. It states the definition, , taxonomy, properties, outcome of cyberbullying, roles in cyberbullying  along with other forms of bullying and different offensive behavior in social media. This article also shows the latest popular benchmark datasets on cyberbullying, along with their number of classes (Binary/Multiple), reviewing the state-of-the-art methods to detect cyberbullying and abusive content on social media and discuss the factors that drive offenders to indulge in offensive activity, preventive actions to avoid online toxicity, and various cyber laws in different countries. Finally, we identify and discuss the challenges, solutions, additionally future research directions that serve as a reference to overcome cyberbullying in social media

Predictive Analytics on Emotional Data Mined from Digital Social Networks with a Focus on Financial Markets

This dissertation is a cumulative dissertation and is comprised of five articles. User-Generated Content (UGC) comprises a substantial part of communication via social media. In this dissertation, UGC that carries and facilitates the exchange of emotions is referred to as “emotional data.” People “produce” emotional data, that is, they express their emotions via tweets, forum posts, blogs, and so on, or they “consume” it by being influenced by expressed sentiments, feelings, opinions, and the like. Decisions often depend on shared emotions and data – which again lead to new data because decisions may change behaviors or results. “Emotional Data Intelligence” ultimately seeks an answer to the question of how all the different emotions expressed in public online sources influence decision-making processes. The overarching research topic of this dissertation follows the question whether network structures and emotional sentiment data extracted from digital social networks contain predictive information or they are just noise. Underlying data was collected from different social media sources, such as Twitter, blogs, message boards, or online news and social networking sites, such as Xing. By means of methodologies of social network analysis (SNA), sentiment analysis, and predictive analysis the individual contributions of this dissertation study whether sentiment data from social media or online social networking structures can predict real-world behaviors. The focus lies on the analysis of emotional data and network structures and its predictive power for financial markets. With the formal construction of the data analyses methodologies introduced in the individual contributions this dissertation contributes to the theories of social network analysis, sentiment analysis, and predictive analytics

General Sentiment Decomposition: opinion mining based on raw Natural Language text

The importance of person-to-person communication about a certain topic (Word of Mouth) is growing day by day, especially for decision-makers. These phenomena can be directly observed in online social networks. For example, the rise of influencers and social media managers. If more people talk about a specific product, then more people are encouraged to buy it and vice versa. Forby, those people usually leave a review for it. Such a review will directly impact the product, and this effect is amplified proportionally to how much the reviewer is considered to be trustworthy by the potential new customer. Furthermore, considering the negative reporting bias, it is easy to understand how customer satisfaction is of absolute interest for a company (as well as citizens' trust is for a politician). Textual data have then proved extremely useful, but they are complex, as the language is. For that, many approaches focus more on producing well-performing classifiers and ignore the highly complex interpretability of their models. Instead, we propose a framework able to produce a good sentiment classifier with a particular focus on the model interpretability. After analyzing the impact of Word of Mouth on earnings and the related psychological aspects, we propose an algorithm to extract the sentiment from a Natural Language text corpus. The combined approach of Neural Networks, characterized by high predictive power but at the cost of complex interpretation (usually considered as black-boxes), with more straightforward and informative models, allows not only to predict how much a sentence is positive (negative) but also to quantify a sentiment with a numeric value. In fact, the General Sentiment Decomposition (GSD) framework that we propose is based on a combination of Threshold-based Naive Bayes (an improved version of the original algorithm), SentiWordNet (an enriched Lexical Database for Sentiment Analysis tasks), and the Words Embeddings features (a high dimensional representation of words) that directly comes from the usage of Neural Networks. Moreover, using the GSD framework, we assess an objective sentiment scoring that improves the results' interpretation in many fields. For example, it is possible to identify specific critical sectors that require intervention to improve the offered services, find the company's strengths (useful for advertising campaigns), and, if time information is present, analyze trends on macro/micro topics. Besides, we have to consider that NL text data can be associated (or not) with a sentiment label, for example: 'positive' or 'negative'. To support further decision-making, we apply the proposed method to labeled (Booking.com, TripAdvisor.com) and unlabelled (Twitter.com) data, analyzing the sentiment of people who discuss a particular issue. In this way, we identify the aspects perceived as critical by the people concerning the "feedback" they publish on the web and quantify how happy (or not) they are about a specific problem. In particular, for Booking.com and TripAdvisor.com, we focus on customer satisfaction, whilst for Twitter.com, the main topic is climate change

5th International Conference on Advanced Research Methods and Analytics (CARMA 2023)

Research methods in economics and social sciences are evolving with the increasing availability of Internet and Big Data sources of information. As these sources, methods, and applications become more interdisciplinary, the 5th International Conference on Advanced Research Methods and Analytics (CARMA) is a forum for researchers and practitioners to exchange ideas and advances on how emerging research methods and sources are applied to different fields of social sciences as well as to discuss current and future challenges.Martínez Torres, MDR.; Toral Marín, S. (2023). 5th International Conference on Advanced Research Methods and Analytics (CARMA 2023). Editorial Universitat Politècnica de València. https://doi.org/10.4995/CARMA2023.2023.1700

Graph-based approaches for semi-supervised and cross-domain sentiment analysis

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of PhilosophyThe rapid development of Internet technologies has resulted in a sharp increase in the number of Internet users who create content online. Usergenerated content often represents people's opinions, thoughts, speculations and sentiments and is a valuable source of information for companies, organisations and individual users. This has led to the emergence of the eld of sentiment analysis, which deals with the automatic extraction and classi cation of sentiments expressed in texts. Sentiment analysis has been intensively researched over the last ten years, but there are still many issues to be addressed. One of the main problems is the lack of labelled data necessary to carry out precise supervised sentiment classi cation. In response, research has moved towards developing semi-supervised and crossdomain techniques. Semi-supervised approaches still need some labelled data and their e ectiveness is largely determined by the amount of these data, whereas cross-domain approaches usually perform poorly if training data are very di erent from test data. The majority of research on sentiment classi cation deals with the binary classi cation problem, although for many practical applications this rather coarse sentiment scale is not su cient. Therefore, it is crucial to design methods which are able to perform accurate multiclass sentiment classi cation. iii The aims of this thesis are to address the problem of limited availability of data in sentiment analysis and to advance research in semi-supervised and cross-domain approaches for sentiment classi cation, considering both binary and multiclass sentiment scales. We adopt graph-based learning as our main method and explore the most popular and widely used graph-based algorithm, label propagation. We investigate various ways of designing sentiment graphs and propose a new similarity measure which is unsupervised, easy to compute, does not require deep linguistic analysis and, most importantly, provides a good estimate for sentiment similarity as proved by intrinsic and extrinsic evaluations. The main contribution of this thesis is the development and evaluation of a graph-based sentiment analysis system that a) can cope with the challenges of limited data availability by using semi-supervised and crossdomain approaches b) is able to perform multiclass classi cation and c) achieves highly accurate results which are superior to those of most stateof- the-art semi-supervised and cross-domain systems. We systematically analyse and compare semi-supervised and cross-domain approaches in the graph-based framework and propose recommendations for selecting the most pertinent learning approach given the data available. Our recommendations are based on two domain characteristics, domain similarity and domain complexity, which were shown to have a signi cant impact on semi-supervised and cross-domain performance

PROBABILISTIC MODELS FOR IDENTIFYING AND EXPLAINING CONTROVERSY

Navigating controversial topics on the Web encourages social awareness, supports civil discourse, and promotes critical literacy. While search of controversial topics particularly requires users to use their critical literacy skills on the content, educating people to be more critical readers is known to be a complex and long-term process. Therefore, we are in need of search engines that are equipped with techniques to help users to understand controversial topics by identifying them and explaining why they are controversial. A few approaches for identifying controversy have worked reasonably well in practice, but they are narrow in scope and exhibit limited performance. In this thesis, we first focus on understanding the theoretical grounding of the state-of-the-art algorithm. We derive an underlying probabilistic model that explains the state-of-the-art controversy detection algorithm. We revisit the properties and assumptions from the derived model, and propose new methods to identify controversy on Webpages. We then point out that the current approaches for controversy detection do not consider time while controversy is a dynamically changing phenomenon. This causes current methods to have delays in recognizing emerging controversial topics or exaggerated effects on outdated controversies. We address time-adaptable controversy detection by estimating the dynamically-changing controversy trend of topic by interpolating the observed level of contention and the public interest over time on the topic. Finally, we offer a method that explains controversy by generating a summary of each stance. Our method ranks social media postings using a score of how likely it is that the given post can be a representative summary of controversy

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

Tune your brown clustering, please

Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown