8,299 research outputs found
Persian topic detection based on Human Word association and graph embedding
In this paper, we propose a framework to detect topics in social media based
on Human Word Association. Identifying topics discussed in these media has
become a critical and significant challenge. Most of the work done in this area
is in English, but much has been done in the Persian language, especially
microblogs written in Persian. Also, the existing works focused more on
exploring frequent patterns or semantic relationships and ignored the
structural methods of language. In this paper, a topic detection framework
using HWA, a method for Human Word Association, is proposed. This method uses
the concept of imitation of mental ability for word association. This method
also calculates the Associative Gravity Force that shows how words are related.
Using this parameter, a graph can be generated. The topics can be extracted by
embedding this graph and using clustering methods. This approach has been
applied to a Persian language dataset collected from Telegram. Several
experimental studies have been performed to evaluate the proposed framework's
performance. Experimental results show that this approach works better than
other topic detection methods
Extracting News Events from Microblogs
Twitter stream has become a large source of information for many people, but
the magnitude of tweets and the noisy nature of its content have made
harvesting the knowledge from Twitter a challenging task for researchers for a
long time. Aiming at overcoming some of the main challenges of extracting the
hidden information from tweet streams, this work proposes a new approach for
real-time detection of news events from the Twitter stream. We divide our
approach into three steps. The first step is to use a neural network or deep
learning to detect news-relevant tweets from the stream. The second step is to
apply a novel streaming data clustering algorithm to the detected news tweets
to form news events. The third and final step is to rank the detected events
based on the size of the event clusters and growth speed of the tweet
frequencies. We evaluate the proposed system on a large, publicly available
corpus of annotated news events from Twitter. As part of the evaluation, we
compare our approach with a related state-of-the-art solution. Overall, our
experiments and user-based evaluation show that our approach on detecting
current (real) news events delivers a state-of-the-art performance
From the User to the Medium: Neural Profiling Across Web Communities
Online communities provide a unique way for individuals to access information
from those in similar circumstances, which can be critical for health
conditions that require daily and personalized management. As these groups and
topics often arise organically, identifying the types of topics discussed is
necessary to understand their needs. As well, these communities and people in
them can be quite diverse, and existing community detection methods have not
been extended towards evaluating these heterogeneities. This has been limited
as community detection methodologies have not focused on community detection
based on semantic relations between textual features of the user-generated
content. Thus here we develop an approach, NeuroCom, that optimally finds dense
groups of users as communities in a latent space inferred by neural
representation of published contents of users. By embedding of words and
messages, we show that NeuroCom demonstrates improved clustering and identifies
more nuanced discussion topics in contrast to other common unsupervised
learning approaches
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
The number of approved patents worldwide increases rapidly each year, which
requires new patent analytics to efficiently mine the valuable information
attached to these patents. Vector space model (VSM) represents documents as
high-dimensional vectors, where each dimension corresponds to a unique term.
While originally proposed for information retrieval systems, VSM has also seen
wide applications in patent analytics, and used as a fundamental tool to map
patent documents to structured data. However, VSM method suffers from several
limitations when applied to patent analysis tasks, such as loss of
sentence-level semantics and curse-of-dimensionality problems. In order to
address the above limitations, we propose a patent analytics based on feature
vector space model (FVSM), where the FVSM is constructed by mapping patent
documents to feature vectors extracted by convolutional neural networks (CNN).
The applications of FVSM for three typical patent analysis tasks, i.e., patents
similarity comparison, patent clustering, and patent map generation are
discussed. A case study using patents related to Internet of Things (IoT)
technology is illustrated to demonstrate the performance and effectiveness of
FVSM. The proposed FVSM can be adopted by other patent analysis studies to
replace VSM, based on which various big data learning tasks can be performed
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