Mikalai Tsytsarau

Survey on mining subjective data on the web

In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers’ attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.

Social Listening of City Scale Events Using the Streaming Linked Data Framework

City-scale events may easily attract half a million of visitors in hundreds of venues over just a few days. Which are the most attended venues? What do visitors think about them? How do they feel before, during and after the event? These are few of the questions a city-scale event manger would like to see answered in real-time. In this paper, we report on our experience in social listening of two city-scale events (London Olympic Games 2012, and Milano Design Week 2013) using the Streaming Linked Data Framework.

Dynamics of news events and social media reaction

The analysis of social sentiment expressed on the Web is becoming increasingly relevant to a variety of applications, and it is important to understand the underlying mechanisms which drive the evolution of sentiments in one way or another, in order to be able to predict these changes in the future. In this paper, we study the dynamics of news events and their relation to changes of sentiment expressed on relevant topics. We propose a novel framework, which models the behavior of news and social media in response to events as a convolution between events importance and media response function, specific to media and event type. This framework is suitable for detecting time and duration of events, as well as their impact and dynamics, from time series of publication volume. These data can greatly enhance events analysis; for instance, they can help distinguish important events from unimportant, or predict sentiment and stock market shifts. As an example of such application, we extracted news events for a variety of topics and then correlated this data with the corresponding sentiment time series, revealing the connection between sentiment shifts and event dynamics.

Scalable discovery of contradictions on the web

Our study addresses the problem of large-scale contradiction detection and management, from data extracted from the Web. We describe the first systematic solution to the problem, based on a novel statistical measure for contradictions, which exploits first- and second-order moments of sentiments. Our approach enables the interactive analysis and online identification of contradictions under multiple levels of time granularity. The proposed algorithm can be used to analyze and track opinion evolution over time and to identify interesting trends and patterns. It uses an incrementally updatable data structure to achieve computational efficiency and scalability. Experiments with real datasets show promising time performance and accuracy.

Managing Diverse Sentiments at Large Scale

The large-scale aggregation and analysis of user opinions is becoming increasingly relevant to a variety of applications, from detecting social mood on some political topics to tracking their sentiment changes related to events. The analysis of diverse sentiments is another important application, which becomes possible based on the ability of modern methods to capture sentiment polarity on various topics with high precision and on the ever-growing scale. Therefore, there is a need for a scalable way of sentiment aggregation with respect to the time dimension, which stores enough information to preserve diversity, and which allows statistically accurate analysis of sentiment trends and opinion shifts. In this paper, we are focusing on the novel problem of aggregating diverse sentiments at a large scale, based on data sources that are continuously updated. First, we develop a theoretical framework that models sentiment diversity (contradiction) and defines two types of contradictions, depending on the distribution of sentiments over time. Second, we introduce novel measures that capture sentiment diversity from aggregated sentiment statistics. Third, we develop robust and scalable indexing and storage methods for diverse sentiments. Finally, we propose an adaptive approach for identifying contradictions at different time scales. The experimental evaluation demonstrates the effectiveness of the proposed method of capturing contradictions and its superiority over relational databases in real-world scenarios.

Diverse Dimension Decomposition of an Itemset Space

We introduce the problem of diverse dimension decomposition in transactional databases. A dimension is a set of mutually-exclusive item sets, and our problem is to find a decomposition of the item set space into dimensions, which are orthogonal to each other, and that provide high coverage of the input database. The mining framework we propose effectively represents a dimensionality-reducing transformation from the space of all items to the space of orthogonal dimensions. Our approach relies on information-theoretic concepts, and we are able to formulate the dimension-finding problem with a single objective function that simultaneously captures constraints on coverage, exclusivity and orthogonality. We describe an efficient greedy method for finding diverse dimensions from transactional databases. The experimental evaluation of the proposed approach using two real datasets, flickr and delicious, demonstrates the effectiveness of our solution. Although we are motivated by the applications in the collaborative tagging domain, we believe that the mining task we introduce in this paper is general enough to be useful in other application domains.

Towards a Framework for Detecting and Managing Opinion Contradictions

Sentiment Analysis gains in interest due to the large amount of potential applications and the increasing number of opinions expressed in particular in the Web. The focus of this paper is the development of a framework on top of sentiment analysis for detecting contradictions. First, we introduce a statistical model of contradictions based on a mean value and the variance of sentiments among different posts. It can be used to analyze and track sentiment evolution over time, to identify interesting trends and patterns or even to enable argument extraction. Using synthetic datasets, we demonstrate the effectiveness of our method in capturing contradictions on noisy data. Inspired by this model, which has proven to be effective and efficient for numeric sentiments, we are trying to generalize it for arbitrary opinion data and outline a universal framework which can be efficiently used on a large scale. We discuss various problems and challenges of such a formulation and outline the scope of our future work in this direction.

Diverse dimension decomposition for itemset spaces

We introduce the problem of diverse dimension decomposition in transactional databases, where a dimension is a set of mutually exclusive itemsets. The problem we consider requires to find a decomposition of the itemset space into dimensions, which are orthogonal to each other and which provide high coverage of the input database. The mining framework we propose can be interpreted as a dimensionality-reducing transformation from the space of all items to the space of orthogonal dimensions. Relying on information-theoretic concepts, we formulate the diverse dimension decomposition problem with a single objective function that simultaneously captures constraints on coverage, exclusivity, and orthogonality. We show that our problem is NP-hard, and we propose a greedy algorithm exploiting the well-known FP-tree data structure. Our algorithm is equipped with strategies for pruning the search space deriving directly from the objective function. We also prove a property that allows assessing the level of informativeness for newly added dimensions, thus allowing to define criteria for terminating the decomposition. We demonstrate the effectiveness of our solution by experimental evaluation on synthetic datasets with known dimension and three real-world datasets, flickr, del.icio.us and dblp. The problem we study is largely motivated by applications in the domain of collaborative tagging; however, the mining task we introduce in this paper is useful in other application domains as well.

Twindex Fuorisalone: Social Listening of Milano during Fuorisalone 2013

Fuorisalone during Milano Design Week, with almost three thousands events spread around more than six hundreds venues, attracts half a million visitors: what do they say and feel about those events? Twindex Fuorisalone is a mash-up that listens what all those visitors posted on Twitter and Instragram in that week. In this paper, we briefly report on how Twindex Fuorisalone works and on its ability to listen in real-time the pulse of Fuorisalone on social media.