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method based on semi-supervised learning to get focuses of social topics in a large amount of text. We develop a novel keyword extraction method named NATF-PDF, which is based on TFPDF algorithm, combined with supervised learning theory for keyword extraction. We compare its performance with TFIDF in comparison, and the
this problem by automatically dividing the social network of a Twitter user into personal cliques, and annotating each clique with keywords to identify the common ground of a clique. Our proposed clique annotation method extracts keywords from the tweet history of the clique members and individually weights the extracted
In this paper, we propose an expert search scheme in social networks. The proposed scheme updates a profile by analyzing recent activities, and considers the reliability scores of users and users' ratings that are computed by the updated profile. A user's profile is created by extracting a keyword from the recent
We demonstrate the effectiveness that machine learning can bring to improving social media platforms through a case study on Twitter trending topics. Social media relies heavily on tagging and often does not take advantage of machine learning advances. Twitter is no exception. Individual tweets are identified as being
Social platforms have become one of the popular mediums of information sharing and communication over the Internet today. People share all types of contents such as text, images, audio and video using these social platforms. Though information gained using these social platforms can be very useful for people around
Recently, a huge wave of social media has generated significant impact in people's perceptions about technological domains. They are captured in several blogs/forums, where the themes relate to products of several companies. One of the companies can be interested to track them as resources for customer perceptions and
poisoned by clever spammers who avoid spam keywords and add many innocuous words in their emails. Also, Bayesian spam filters need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist in spam detection. In order to develop an accurate
information is deficient and noisy on YouTube. In this paper, we propose the novel dual updating method for YouTube video topic discovery. We first enhance the document representation for each video with its related videos, then we extract meaningful topics via keyword cores, at last, the video response links and the
Addressing the problem of spam emails in the Internet, this paper presents a comparative study on Nai??ve Bayes and Artificial Neural Networks (ANN) based modeling of spammer behavior. Keyword-based spam email filtering techniques fall short to model spammer behavior as the spammer constantly changes tactics to
easily poisoned by avoiding spam keywords and adding many innocuous keywords in the emails. In addition, they need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist spam detection. In order to develop an accurate and user-friendly
Question Understanding of Chinese Question-Answering System generally includes steps such as: word segmentation, POS Tagging, keywords expansion, information retrieval etc. The extended keyword set usually has redundant messages and part of the words and phrases may be not relevant to the question. Consequently
Online Social Networks (OSNs) such as Facebook and Twitter are the fastest growing online entities. Because they do not require much authentication for a user to create an account, they are susceptible to social spam attacks. These low-quality, unsolicited, and unwanted bulk messages commonly originate from Social
problems early or in their entirety through those channels because subscribers typically do not call a call center until they are certain the problem was caused by a network. In this paper, we discuss a way to monitor a social networking service (SNS) (Twitter in particular) to find out about problems that affect subscribers
Manual tagging has an important impact to performance of image/video searching by keyword. However, users usually mark tags only landmarks are as on only a few images in library and leave most contents untagged. If landmarks from different places are look alike, it is hard to distinguish even though surroundings are
comprehensive and quality feeds for real-time event detection. In this paper, we present a novel adaptive keyword identification approach to retrieve a greater amount of event relevant content. This approach continuously monitors emerging hashtags and rates them by their similarity to specific pre-defined event hashtags using TF
A story is defined as "an actor(s) taking action(s) that culminates in a resolution(s)." In this paper, we investigate the utility of standard keyword based features, statistical features based on shallow-parsing (such as density of POS tags and named entities), and a new set of semantic features to
The growing popularity of social network services has led to many studies of various phenomena in this area. However, most of this research has been conducted using English language data, and relatively little has considered Korean. In this paper, we demonstrate a systematic analysis framework using Korean Twitter
public display raises specific challenges that may limit the applicability of existing recommender systems. In this paper, we explore the creation of a recommender system for public situated displays that is able to autonomously select relevant content from Internet sources using keywords as input. This type of recommender
different keywords or entities to form a more comprehensive view. The proposed solution aims to combine social media data and semantic linked data to extract relevant information and capture the relationship among the entities from content shared by the audience. With a targeted audience profiling, company is able to spend
In recent years, tagging methods have been widely used on the Web for multimedia search and recommendation systems. Social tags are the keywords annotated by users to the multimedia objects. These tags contain the information which allows multimedia objects to be located and classified. Unfortunately, irrelevant
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