Generating Personalised and Opinionated Review Summaries

Abstract. This paper describes a novel approach for summarising usergenerated reviews for the purpose of explaining recommendations. We demonstrate our approach using TripAdvisor reviews

Opinion-aware Information Management: Statistical Summarisation and Knowledge Representation of Opinions.

PhDNowadays, an increasing amount of media platforms provide the users with opportunities for sharing their opinions about products, companies or people. In order to support users accessing opinion-based information, and to support engineers building systems that require opinion-aware reasoning, intelligent opinion-aware tools and techniques are needed. This thesis contributes methods and technology for opinion-aware information management from two different perspectives, namely document summarisation and knowledge representation. Document summarisation has been widely investigated as a mean to reduce information overload. This thesis focuses on statistical models for summarisation, with a particular attention to divergence-based models, within the context of opinions. Firstly, topic-based document summarisation is addressed, contributing a study on divergence-based document to summary similarity and the definition of a novel algorithm for summarisation based on sentence removal. Secondly, summarisation models are tailored to opinion-oriented content and shown to be useful also when exploited for different tasks such as sentiment classification. Thirdly, summarisation models are applied to knowledge-oriented data, in order to tackle tasks such as entity summarisation. The comprehensive task addressed is the knowledge-based opinion-aware summarisation of content (free text, facts). This thesis also contributes a broad discussion on knowledge representation of opinions. A thorough study on how to model opinions using traditional techniques, such as Entity-Relationship (ER) modelling, underlines that a high-level, opinion-aware layer of conceptual modelling is useful since it hides away implementation details. A conceptual and logical knowledge representation methodology for modelling opinions is hence proposed, with the purpose of guiding engineers towards the use of best practices during the development of sentiment analysis applications. Specifically, an extension of the traditional ER modelling and the definition of an automatic mapping procedure, to translate opinion-aware components of the conceptual model into a relational model, help achieving a clear separation between conceptual and logical modelling. The mapping procedure yields an automatic and replicable methodology to design applications which require opinion-aware reasoning

Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past decade or so, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of relatively recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of Natural Language Processing, with an emphasis on different evaluation methods and the relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118 pages, 8 figures, 1 tabl

A Ranking Approach to Summarising Twitter Home Timelines

The rise of social media services has changed the ways in which users can communicate and consume content online. Whilst online social networks allow for fast and convenient delivery of knowledge, users are prone to information overload when too much information is presented for them to read and process. Automatic text summarisation is a tool to help mitigate information overload. In automatic text summarisation, short summaries are generated algorithmically from extended text, such as news articles or scientific papers. This thesis addresses the challenges in applying text summarisation to the Twitter social network. It also goes beyond text, exploiting additional information that is unique to social networks to create summaries which are personal to an intended reader. Unlike previous work in tweet summarisation, the experiments here address the home timelines of readers, which contain the incoming posts from authors to whom they have explicitly subscribed. A novel contribution is made in this work the form of a large gold standard ($19,350$ tweets), the majority of which will be shared with the research community. The gold standard is a collection of timelines that have been subjectively annotated by the readers to whom they belong, allowing fair evaluation of summaries which are not limited to tweets of general interest, but which are specific to the reader. Where the home timeline is used by professional users for social media analysis, automatic text summarisation can be applied to give results which beat all baselines. In the general case, where no limitation is placed on the types of readers, personalisation features which exploit the relationship between author and reader and the reader's own previous posts, were shown to outperform both automatic text summarisation and all baselines

Bridging democratic gaps or building political brands? : perceptions of representation from the participation of MEPs in social media

This thesis provides a study of the Members of the European Parliament (MEPs) from Ireland, Greece and Cyprus and their use of Social Media Platforms (SMPs). Specifically, the thesis explores MEPs’ motivations for online engagement and the strategies they apply through their social media accounts. The MEPs face a growing demand for approachability and responsiveness within an expanding framework of disconnection and Euroscepticism. The objective is to determine whether they engage online to embrace citizens’ inclusion and the bridging of the democratic gaps which have been exacerbated by the recent crises or whether they focus more on exploiting the promotional advantages of SMPs to enhance their political brand.Central to the thesis purpose is to collect and interpret the perceptions of the MEPs about the functions of accountability, policy discussion and branding. The collection and analysis of data from semi-structured interviews with the MEPs and the qualitative analysis of content from their social media accounts revealed a series of accountability, policy discussion and branding motivations and strategies. These in combination with the post-crisis social media landscape, determine the types, volume and quality of the interactions that MEPs from crisis-inflicted states pursue online during routine (non- campaigning) timeframes.The contribution of the thesis is that it approaches a contemporary phenomenon from an overlooked angle, i.e. how European representatives perceive and prioritise the parameters of online interaction and how this affects the engagement with constituents. Approaching the online activities of the MEPs from a non-traditional theory of representation becomes the key to accommodate their branding incentives and understand and acknowledge their particularities as supranational representatives. This also contributes to outlining an area of theoretical interest where the profiling of representational brands and the use of descriptive features find their place within a participatory framework which surpasses the sanction/reward scheme of the electoral mandate

Social co-creation

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonThe capability of the organisation in capturing customer value of experience (VoE) has led to continuous social interaction and spawned innovative ways to collaborate and co-create with the customers. This notion of reciprocal engagement is referred to as social co-creation. The co-creation paradigm represents value and is referred to as a function of experience other than the product itself. However, there is a critically needs for the organisation to formulate a ‘value capture strategy’ through the lens of social co-creation. It is evident that the fundamental question of the causal relationship between social media and co-creation has not been fully explained. The research developed a value capture framework in order to have a clear need to understand the various perceptions of four important conditions, social co-creation, customer engagement, engagement platform and organisation capability on value capture. The theoretical aspect of ‘Absorptive Capacity Theory (ACT)’ is used to demonstrate the organisational capability in order to recognise, identify, assimilate and implement the VoE in the organisation as part of competitive advantage along with existing of social technologies. In this respect, an original conceptual framework was formulated based on evidence within the current literature where a series of constructs are reported to guide the empirical fieldwork in identifying a ‘value capture strategy’. The research adopted a qualitative methodology for the data collection approach which consequently enabled an exploratory and interpretive investigation. This included three pilot studies, twentyeight semi-structured interviews and one validation phase with experienced senior managers involved in co-creation within the technology and services industry who were regarded as valid respondents. The findings addressed external and internal conditions of value capture framework for VoE as a result of the analysis. The external condition focuses on organisation capability in recognising a customer active participation with the engagement platform. The internal condition enables an organisational strategy to assimilate and implement the VoE through co-creation initiatives in capturing the VoE. The research considers the organisation role as an actioner purposes on using a social co-creation for direct communication as part of the organisations practice. 9 The study not only contributes to the knowledge of social co-creation generally, but also extending the needs for the organisation on considering the internal condition for the organisation to identifies the VoE from customer participation. With that regards, the social co-creation engagement works as the interface before extending on cocreation stages which more direct, in-depth conversations with customers internally. The evidence is presented which confirms that a value capture strategy in cocreation is important for organisations stability and enhanced service delivery. By formulating a value capture framework, it creates a much deeper understanding of how each element were related and correlated to reach potential end result for the organisation. The implications of the study are that organisations should carefully consider the role of social media on engaging with the customers and propose to develop an online engagement network with their customers in order to have more direct and effective communication tools. This would allow them to have the right strategy on selecting the right customer to engage, for the right purposes at the right time is far more important from creating a massive communication.Majlis Amanah Rakyat (MARA-Pasca-Siswazah 2013

Role of emotion in information retrieval

The main objective of Information Retrieval (IR) systems is to satisfy searchers’ needs. A great deal of research has been conducted in the past to attempt to achieve a better insight into searchers’ needs and the factors that can potentially influence the success of an Information Retrieval and Seeking (IR&S) process. One of the factors which has been considered is searchers’ emotion. It has been shown in previous research that emotion plays an important role in the success of an IR&S process, which has the purpose of satisfying an information need. However, these previous studies do not give a sufficiently prominent position to emotion in IR, since they limit the role of emotion to a secondary factor, by assuming that a lack of knowledge (the need for information) is the primary factor (the motivation of the search). In this thesis, we propose to treat emotion as the principal factor in the system of needs of a searcher, and therefore one that ought to be considered by the retrieval algorithms. We present a more realistic view of searchers’ needs by considering not only theories from information retrieval and science, but also from psychology, philosophy, and sociology. We extensively report on the role of emotion in every aspect of human behaviour, both at an individual and social level. This serves not only to modify the current IR views of emotion, but more importantly to uncover social situations where emotion is the primary factor (i.e., source of motivation) in an IR&S process. We also show that the emotion aspect of documents plays an important part in satisfying the searcher’s need, in particular when emotion is indeed a primary factor. Given the above, we define three concepts, called emotion need, emotion object and emotion relevance, and present a conceptual map that utilises these concepts in IR tasks and scenarios. In order to investigate the practical concepts such as emotion object and emotion relevance in a real-life application, we first study the possibility of extracting emotion from text, since this is the first pragmatic challenge to be solved before any IR task can be tackled. For this purpose, we developed a text-based emotion extraction system and demonstrate that it outperforms other available emotion extraction approaches. Using the developed emotion extraction system, the usefulness of the practical concepts mentioned above is studied in two scenarios: movie recommendation and news diversification. In the movie recommendation scenario, two collaborative filtering (CF) models were proposed. CF systems aim to recommend items to a user, based on the information gathered from other users who have similar interests. CF techniques do not handle data sparsity well, especially in the case of the cold start problem, where there is no past rating for an item. In order to predict the rating of an item for a given user, the first and second models rely on an extension of state-of-the-art memory-based and model-based CF systems. The features used by the models are two emotion spaces extracted from the movie plot summary and the reviews made by users, and three semantic spaces, namely, actor, director, and genre. Experiments with two MovieLens datasets show that the inclusion of emotion information significantly improves the accuracy of prediction when compared with the state-of-the-art CF techniques, and also tackles data sparsity issues. In the news retrieval scenario, a novel way of diversifying results, i.e., diversifying based on the emotion aspect of documents, is proposed. For this purpose, two approaches are introduced to consider emotion features for diversification, and they are empirically tested on the TREC 678 Interactive Track collection. The results show that emotion features are capable of enhancing retrieval effectiveness. Overall, this thesis shows that emotion plays a key role in IR and that its importance needs to be considered. At a more detailed level, it illustrates the crucial part that emotion can play in • searchers, both as a primary (emotion need) and secondary factor (influential role) in an IR&S process; • enhancing the representation of a document using emotion features (emotion object); and finally, • improving the effectiveness of IR systems at satisfying searchers’ needs (emotion relevance)

Sentiment analysis and real-time microblog search

This thesis sets out to examine the role played by sentiment in real-time microblog search. The recent prominence of the real-time web is proving both challenging and disruptive for a number of areas of research, notably information retrieval and web data mining. User-generated content on the real-time web is perhaps best epitomised by content on microblogging platforms, such as Twitter. Given the substantial quantity of microblog posts that may be relevant to a user query at a given point in time, automated methods are required to enable users to sift through this information. As an area of research reaching maturity, sentiment analysis offers a promising direction for modelling the text content in microblog streams. In this thesis we review the real-time web as a new area of focus for sentiment analysis, with a specific focus on microblogging. We propose a system and method for evaluating the effect of sentiment on perceived search quality in real-time microblog search scenarios. Initially we provide an evaluation of sentiment analysis using supervised learning for classi- fying the short, informal content in microblog posts. We then evaluate our sentiment-based filtering system for microblog search in a user study with simulated real-time scenarios. Lastly, we conduct real-time user studies for the live broadcast of the popular television programme, the X Factor, and for the Leaders Debate during the Irish General Election. We find that we are able to satisfactorily classify positive, negative and neutral sentiment in microblog posts. We also find a significant role played by sentiment in many microblog search scenarios, observing some detrimental effects in filtering out certain sentiment types. We make a series of observations regarding associations between document-level sentiment and user feedback, including associations with user profile attributes, and users’ prior topic sentiment

Social Media and the Mediating Role of Perceived Authenticity in Covert Celebrity Endorsement: Influencing Factors

This thesis analyses the factors that influence the celebrity endorser’s perceived authenticity and its impact on the promoted brand in covert social media marketing. To examine consumer behaviour, the Persuasion Knowledge Model and Attribution Theory were integrated, and a theoretical framework was then developed. In total, 653 social media users were recruited to participate in the research, and structural equation modelling was conducted to test the proposed model. The results confirm that (1) activated persuasion knowledge negatively influences celebrity endorser’s perceived authenticity in covert social media marketing; (2) celebrity-brand congruity does not have a significant impact on the endorser’s perceived authenticity; (3) celebrity’s expertise positively influences the celebrity endorser’s perceived authenticity when endorsing products related to his or her area of expertise; (4) the celebrity’s perceived attractiveness has a positive impact on the celebrity’s perceived authenticity when endorsing attractiveness enhancing products covertly in social media; and (5) perceived authenticity of a celebrity endorser positively influences brand attitudes and, consequently, behavioural intentions. Both theoretical and managerial implications are drawn, suggesting directions for future studies