An important part of our information-gathering behavior has always been to find out what other people think. With the growing availability and popularity of opinion-rich resources such as online review sites and personal blogs, new opportunities and challenges arise as people now can, and do, actively use information technologies to seek out and understand the opinions of others. The sudden eruption of activity in the area of opinion mining and sentiment analysis, which deals with the computational treatment of opinion, sentiment, and subjectivity in text, has thus occurred at least in part as a direct response to the surge of interest in new systems that deal directly with opinions as a first-class object.

This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems. Our focus is on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis. We include material on summarization of evaluative text and on broader issues regarding privacy, manipulation, and economic impact that the development of opinion-oriented information-access services gives rise to. To facilitate future work, a discussion of available resources, benchmark datasets, and evaluation campaigns is also provided.

/pdf/opinion-mining-and-sentiment-analysis-3v5ya3qqiw.pdf

Opinion Mining and Sentiment Analysis

Many "big data" applications must act on data in real time. Running these applications at ever-larger scales requires parallel platforms that automatically handle faults and stragglers. Unfortunately, current distributed stream processing models provide fault recovery in an expensive manner, requiring hot replication or long recovery times, and do not handle stragglers. We propose a new processing model, discretized streams (D-Streams), that overcomes these challenges. D-Streams enable a parallel recovery mechanism that improves efficiency over traditional replication and backup schemes, and tolerates stragglers. We show that they support a rich set of operators while attaining high per-node throughput similar to single-node systems, linear scaling to 100 nodes, sub-second latency, and sub-second fault recovery. Finally, D-Streams can easily be composed with batch and interactive query models like MapReduce, enabling rich applications that combine these modes. We implement D-Streams in a system called Spark Streaming.

/pdf/discretized-streams-fault-tolerant-streaming-computation-at-59e0rdpnuz.pdf

Discretized streams: fault-tolerant streaming computation at scale

We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the underlying hyperbolic geometry. Conversely, we show that if a network has some metric structure, and if the network degree distribution is heterogeneous, then the network has an effective hyperbolic geometry underneath. We then establish a mapping between our geometric framework and statistical mechanics of complex networks. This mapping interprets edges in a network as noninteracting fermions whose energies are hyperbolic distances between nodes, while the auxiliary fields coupled to edges are linear functions of these energies or distances. The geometric network ensemble subsumes the standard configuration model and classical random graphs as two limiting cases with degenerate geometric structures. Finally, we show that targeted transport processes without global topology knowledge, made possible by our geometric framework, are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure.

/pdf/hyperbolic-geometry-of-complex-networks-3tgyz5lnel.pdf

Hyperbolic geometry of complex networks

Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

/pdf/higher-order-organization-of-complex-networks-3n5pj1c6nh.pdf

Higher-order organization of complex networks

A method and system for providing targeted advertisements over a networked media delivery system, especially interactive television networks, the system comprising tracking and storing viewer selections, analyzing the selections, and delivering targeted advertisements that appeal to the particular subscriber making the selections, the system including a merge processor, a file server, a profile processor, and a broadcast server contained in a head end in communication with a plurality of set-top boxes through a distribution network. Based on a subscriber's viewing habits and account information, the present invention delivers different, customized advertisements to different viewers watching the same program or channel. The present invention delivers the advertisements as either still frame bit maps or as video streams advertisement insertion in a playlist or a broadcast media program.

Method and system for providing targeted advertisements

Systems and methods for verifying relevance between terms and Web site contents are described. In one aspect, site contents from a bid URL are retrieved. Expanded term(s) semantically and/or contextually related to bid term(s) are calculated. Content similarity and expanded similarity measurements are calculated from respective combinations of the bid term(s), the site contents, and the expanded terms. Category similarity measurements between the expanded terms and the site contents are determined in view of a trained similarity classifier. The trained similarity classifier having been trained from mined web site content associated with directory data. A confidence value providing an objective measure of relevance between the bid term(s) and the site contents is determined from the content, expanded, and category similarity measurements evaluating the multiple similarity scores in view of a trained relevance classifier model.

Verifying relevance between keywords and web site contents

A system and related techniques automatically analyze Web search and other activity, to generate locality-selected results based on not just the user's location, but also the business or other content provider's location and the degree of local intent in the user's query. The locality or region to the user may be identified, for instance, by the presence of geographic clues or indicators in the general content of the Web site, for instance, the presence of ZIP codes, telephone numbers, town names or other semantic or other indicators which have some geographic connotation in search terms, key words or other query or semantic inputs. The user's degree of local intent may be automatically analyzed as well, for instance by the presence of terms such as "car repair" or "pizza restaurant" which may suggest the user intends to locate local goods, services or providers. The search service may then access a content database to identify ads or other media or content which match or correspond to the detected locality or region, such as ads for local restaurants, car dealerships, physicians or other services or products, and which correspond in location, working radius and degree of localness to the user's query. Because the delivery of localized search results according to the invention is specifically filtered for user-driven local intent, advertisements and other media or content may be delivered which better match the user's search objectives.

System and method for automatic generation of search results based on local intention

Understanding goals and preferences behind a user's online activities can greatly help information providers, such as search engine and E-Commerce web sites, to personalize contents and thus improve user satisfaction. Understanding a user's intention could also provide other business advantages to information providers. For example, information providers can decide whether to display commercial content based on user's intent to purchase. Previous work on Web search defines three major types of user search goals for search queries: navigational, informational and transactional or resource [1][7]. In this paper, we focus our attention on capturing commercial intention from search queries and Web pages, i.e., when a user submits the query or browse a Web page, whether he/she is about to commit or in the middle of a commercial activity, such as purchase, auction, selling, paid service, etc. We call the commercial intentions behind a user's online activities as OCI (Online Commercial Intention). We also propose the notion of "Commercial Activity Phase" (CAP), which identifies in which phase a user is in his/her commercial activities: Research or Commit. We present the framework of building machine learning models to learn OCI based on any Web page content. Based on that framework, we build models to detect OCI from search queries and Web pages. We train machine learning models from two types of data sources for a given search query: content of algorithmic search result page(s) and contents of top sites returned by a search engine. Our experiments show that the model based on the first data source achieved better performance. We also discover that frequent queries are more likely to have commercial intention. Finally we propose our future work in learning richer commercial intention behind users' online activities.

/pdf/detecting-online-commercial-intention-oci-45y9ie8eq2.pdf

Detecting online commercial intention (OCI)

A social network visualization and mining system that includes a visualization application for mining social networks of users in an online social network. This visualization can be used to mine the social network for additional information and intelligence. The social network is displaying in graphical form, such as a node-link graph, with a center node representing the social network of a user being examined, and secondary nodes represent the primary user's friends. Lines represent links between the primary user and his friends, while various visualization features such as line thickness, line color, and text size are used to easily identify the type of relationship between users. The system also includes a topics visualization module, which builds and displays a social network based on a certain topic or keyword that is entered by the application user. A demographic prediction module examines a user's social network to predict demographics of users.

Visualization application for mining of social networks

Systems and methods for analyzing the content of online conversations using data mining technologies are provided. Methods and systems for utilizing the results of data mining technology implementation to retrieve relevant advertising content and/or other relevant information for display in association with the content of an online conversation are also provided.

Ying Li

Papers

Verifying relevance between keywords and web site contents

System and method for automatic generation of search results based on local intention

Detecting online commercial intention (OCI)

Visualization application for mining of social networks

System and method for utilizing the content of an online conversation to select advertising content and/or other relevant information for display