473 research outputs found
An efficient Particle Swarm Optimization approach to cluster short texts
This is the author’s version of a work that was accepted for publication in Information Sciencies. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences, VOL 265, MAY 1 2014 DOI 10.1016/j.ins.2013.12.010.Short texts such as evaluations of commercial products, news, FAQ's and scientific abstracts are important resources on the Web due to the constant requirements of people to use this on line information in real life. In this context, the clustering of short texts is a significant analysis task and a discrete Particle Swarm Optimization (PSO) algorithm named CLUDIPSO has recently shown a promising performance in this type of problems. CLUDIPSO obtained high quality results with small corpora although, with larger corpora, a significant deterioration of performance was observed. This article presents CLUDIPSO*, an improved version of CLUDIPSO, which includes a different representation of particles, a more efficient evaluation of the function to be optimized and some modifications in the mutation operator. Experimental results with corpora containing scientific abstracts, news and short legal documents obtained from the Web, show that CLUDIPSO* is an effective clustering method for short-text corpora of small and medium size. (C) 2013 Elsevier Inc. All rights reserved.The research work is partially funded by the European Commission as part of the WIQ-EI IRSES research project (Grant No. 269180) within the FP 7 Marie Curie People Framework and it has been developed in the framework of the Microcluster VLC/Campus (International Campus of Excellence) on Multimodal Intelligent Systems. The research work of the first author is partially funded by the program PAID-02-10 2257 (Universitat Politecnica de Valencia) and CONICET (Argentina).Cagnina, L.; Errecalde, M.; Ingaramo, D.; Rosso, P. (2014). An efficient Particle Swarm Optimization approach to cluster short texts. Information Sciences. 265:36-49. https://doi.org/10.1016/j.ins.2013.12.010S364926
An Improved Similarity Matching based Clustering Framework for Short and Sentence Level Text
Text clustering plays a key role in navigation and browsing process. For an efficient text clustering, the large amount of information is grouped into meaningful clusters. Multiple text clustering techniques do not address the issues such as, high time and space complexity, inability to understand the relational and contextual attributes of the word, less robustness, risks related to privacy exposure, etc. To address these issues, an efficient text based clustering framework is proposed. The Reuters dataset is chosen as the input dataset. Once the input dataset is preprocessed, the similarity between the words are computed using the cosine similarity. The similarities between the components are compared and the vector data is created. From the vector data the clustering particle is computed. To optimize the clustering results, mutation is applied to the vector data. The performance the proposed text based clustering framework is analyzed using the metrics such as Mean Square Error (MSE), Peak Signal Noise Ratio (PSNR) and Processing time. From the experimental results, it is found that, the proposed text based clustering framework produced optimal MSE, PSNR and processing time when compared to the existing Fuzzy C-Means (FCM) and Pairwise Random Swap (PRS) methods
A PSO-based clustering approach assisted by initial clustering information
Clustering of short texts is an important research area because of its applicability in information retrieval and text mining. To this end was proposed CLUDIPSO, a discrete Particle Swarm Optimization algorithm to cluster short texts. Initial results showed that CLUDIPSO has performed well in small collections of short texts. However, later works showed some drawbacks when dealing with larger collections. In this paper we present a hybridization of CLUDIPSO to overcome these drawbacks, by providing information in the initial cycles of the algorithm to avoid a random search and thus speed up the convergence process. This is achieved by using a pre-clustering obtained with the Expectation-Maximization method which is included in the initial population of the algorithm. The results obtained with the hybrid version show a significant improvement over those obtained with the original version.Eje: Workshop Bases de datos y minerÃa de datos (WBDDM)Red de Universidades con Carreras en Informática (RedUNCI
Advances in Meta-Heuristic Optimization Algorithms in Big Data Text Clustering
This paper presents a comprehensive survey of the meta-heuristic optimization algorithms on the text clustering applications and highlights its main procedures. These Artificial Intelligence (AI) algorithms are recognized as promising swarm intelligence methods due to their successful ability to solve machine learning problems, especially text clustering problems. This paper reviews all of the relevant literature on meta-heuristic-based text clustering applications, including many variants, such as basic, modified, hybridized, and multi-objective methods. As well, the main procedures of text clustering and critical discussions are given. Hence, this review reports its advantages and disadvantages and recommends potential future research paths. The main keywords that have been considered in this paper are text, clustering, meta-heuristic, optimization, and algorithm
New techniques for Arabic document classification
Text classification (TC) concerns automatically assigning a class (category) label to
a text document, and has increasingly many applications, particularly in the domain
of organizing, for browsing in large document collections. It is typically achieved
via machine learning, where a model is built on the basis of a typically large collection
of document features. Feature selection is critical in this process, since there
are typically several thousand potential features (distinct words or terms). In text
classification, feature selection aims to improve the computational e ciency and
classification accuracy by removing irrelevant and redundant terms (features), while
retaining features (words) that contain su cient information that help with the
classification task.
This thesis proposes binary particle swarm optimization (BPSO) hybridized with
either K Nearest Neighbour (KNN) or Support Vector Machines (SVM) for feature
selection in Arabic text classi cation tasks. Comparison between feature selection
approaches is done on the basis of using the selected features in conjunction with
SVM, Decision Trees (C4.5), and Naive Bayes (NB), to classify a hold out test
set. Using publically available Arabic datasets, results show that BPSO/KNN and
BPSO/SVM techniques are promising in this domain. The sets of selected features
(words) are also analyzed to consider the di erences between the types of features
that BPSO/KNN and BPSO/SVM tend to choose. This leads to speculation concerning
the appropriate feature selection strategy, based on the relationship between
the classes in the document categorization task at hand.
The thesis also investigates the use of statistically extracted phrases of length
two as terms in Arabic text classi cation. In comparison with Bag of Words text
representation, results show that using phrases alone as terms in Arabic TC task
decreases the classification accuracy of Arabic TC classifiers significantly while combining
bag of words and phrase based representations may increase the classification
accuracy of the SVM classifier slightly
Easy over Hard: A Case Study on Deep Learning
While deep learning is an exciting new technique, the benefits of this method
need to be assessed with respect to its computational cost. This is
particularly important for deep learning since these learners need hours (to
weeks) to train the model. Such long training time limits the ability of (a)~a
researcher to test the stability of their conclusion via repeated runs with
different random seeds; and (b)~other researchers to repeat, improve, or even
refute that original work.
For example, recently, deep learning was used to find which questions in the
Stack Overflow programmer discussion forum can be linked together. That deep
learning system took 14 hours to execute. We show here that applying a very
simple optimizer called DE to fine tune SVM, it can achieve similar (and
sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84
times faster hours than deep learning method.
We offer these results as a cautionary tale to the software analytics
community and suggest that not every new innovation should be applied without
critical analysis. If researchers deploy some new and expensive process, that
work should be baselined against some simpler and faster alternatives.Comment: 12 pages, 6 figures, accepted at FSE201
Hybrid Sentiment Classification of Reviews Using Synonym Lexicon and Word embedding
Sentiment analysis is used in extract some useful
information from the given set of documents by
using Natural Language Processing (NLP)
techniques. These techniques have wide scope in
various fields which are dealing with huge
amount of data link e-commerce, business and
market analysis, social media and review impact
of products and movies. Sentiment analysis can
be applied over these data for finding the polarity
of the data like positive, neutral or negative
automatically or many complex sentiments like
happiness, sad, anger, joy, etc. for a particular
product and services based on user reviews.
Sentiment analysis not only able to find the
polarity of the reviews. Sentiment analysis
utilizes machine learning algorithms with
vectorization techniques based on textual
documents to train the classifier models. These
models are later used to perform sentiment
analysis on the given dataset of particular domain
on which the classifier model is trained.
Vectorization is done for text document by using
word embedding based and hybrid vectorization.
The proposed methodology focus on fast and
accurate sentiment prediction with higher
confidence value over the dataset in both Tamil
and English
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