Related papers: Enhanced Semantic Graph Based Approach With Sentim…
We propose a general framework for the recommendation of possible customers (users) to advertisers (e.g., brands) based on the comparison between On-line Social Network profiles. In particular, we represent both user and brand profiles as…
We build a bridge between neural network-based machine learning and graph-based natural language processing and introduce a unified approach to keyphrase, summary and relation extraction by aggregating dependency graphs from links provided…
Automatic Text Summarization strategies have been successfully employed to digest text collections and extract its essential content. Usually, summaries are generated using textual corpora that belongs to the same domain area where the…
Online social networks have emerged as useful tools to communicate or share information and news on a daily basis. One of the most popular networks is Twitter, where users connect to each other via directed follower relationships.…
We describe a knowledge graph derived from Twitter data with the goal of discovering relationships between people, links, and topics. The goal is to filter out noise from Twitter and surface an inside-out view that relies on high quality…
In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments we assisted to the proliferation of new algorithms, methods, and approaches in two areas of…
Emergency-relevant data comes in many varieties. It can be high volume and high velocity, and reaction times are critical, calling for efficient and powerful techniques for data analysis and management. Knowledge graphs represent data in a…
Online social network services provide a platform for human social interactions. Nowadays, many kinds of online interactions generate large-scale social network data. Network analysis helps to mine knowledge and pattern from the…
Traditional search methods primarily depend on string matches, while semantic search targets concept-based matches by recognizing underlying intents and contextual meanings of search terms. Semantic search is particularly beneficial for…
On social media platforms like Twitter, users regularly share their opinions and comments with software vendors and service providers. Popular software products might get thousands of user comments per day. Research has shown that such…
Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender systems (RS). To overcome these challenges and many similar…
Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-based ranking method to extract high-quality phrases from a…
Graph recommendation methods, representing a connected interaction perspective, reformulate user-item interactions as graphs to leverage graph structure and topology to recommend and have proved practical effectiveness at scale. Large…
Information about individuals can help to better understand what they say, particularly in social media where texts are short. Current approaches to modelling social media users pay attention to their social connections, but exploit this…
Semantic information is often represented as the entities and the relationships among them with conventional semantic models. This approach is straightforward but is not suitable for many posteriori requests in semantic data modeling. In…
Social media data are often modeled as heterogeneous graphs with multiple types of nodes and edges. We present a discovery algorithm that first chooses a "background" graph based on a user's analytical interest and then automatically…
This paper focuses on the detection of potentially dangerous tendencies of social media users in an innovative multimodal way. We integrate Natural Language Processing (NLP) and Graph Neural Networks (GNNs) together. Firstly, we apply NLP…
Keyword Extraction is an important task in several text analysis endeavors. In this paper, we present a critical discussion of the issues and challenges ingraph-based keyword extraction methods, along with comprehensive empirical analysis.…
Online sellers and advertisers are recommended keyphrases for their listed products, which they bid on to enhance their sales. One popular paradigm that generates such recommendations is Extreme Multi-Label Classification (XMC), which…
Rapid increase in internet users along with growing power of online review sites and social media has given birth to sentiment analysis or opinion mining, which aims at determining what other people think and comment. Sentiments or Opinions…