998 research outputs found
Arabic Language Sentiment Analysis on Health Services
The social media network phenomenon leads to a massive amount of valuable
data that is available online and easy to access. Many users share images,
videos, comments, reviews, news and opinions on different social networks
sites, with Twitter being one of the most popular ones. Data collected from
Twitter is highly unstructured, and extracting useful information from tweets
is a challenging task. Twitter has a huge number of Arabic users who mostly
post and write their tweets using the Arabic language. While there has been a
lot of research on sentiment analysis in English, the amount of researches and
datasets in Arabic language is limited. This paper introduces an Arabic
language dataset which is about opinions on health services and has been
collected from Twitter. The paper will first detail the process of collecting
the data from Twitter and also the process of filtering, pre-processing and
annotating the Arabic text in order to build a big sentiment analysis dataset
in Arabic. Several Machine Learning algorithms (Naive Bayes, Support Vector
Machine and Logistic Regression) alongside Deep and Convolutional Neural
Networks were utilized in our experiments of sentiment analysis on our health
dataset.Comment: Authors accepted version of submission for ASAR 201
Towards Arabic Alphabet and Numbers Sign Language Recognition
This paper proposes to develop a new Arabic sign language recognition using Restricted Boltzmann Machines and a direct use of tiny images. Restricted Boltzmann Machines are able to code images as a superposition of a limited number of features taken from a larger alphabet. Repeating this process in deep architecture (Deep Belief Networks) leads to an efficient sparse representation of the initial data in the feature space. A complex problem of classification in the input space is thus transformed into an easier one in the feature space. After appropriate coding, a softmax regression in the feature space must be sufficient to recognize a hand sign according to the input image. To our knowledge, this is the first attempt that tiny images feature extraction using deep architecture is a simpler alternative approach for Arabic sign language recognition that deserves to be considered and investigated
Short Text Classification using Contextual Analysis
Peer reviewedPublisher PD
A Hybrid Approach Towards Two Stage Bengali Question Classification Utilizing Smart Data Balancing Technique
Question classification (QC) is the primary step of the Question Answering
(QA) system. Question Classification (QC) system classifies the questions in
particular classes so that Question Answering (QA) System can provide correct
answers for the questions. Our system categorizes the factoid type questions
asked in natural language after extracting features of the questions. We
present a two stage QC system for Bengali. It utilizes one dimensional
convolutional neural network for classifying questions into coarse classes in
the first stage. Word2vec representation of existing words of the question
corpus have been constructed and used for assisting 1D CNN. A smart data
balancing technique has been employed for giving data hungry convolutional
neural network the advantage of a greater number of effective samples to learn
from. For each coarse class, a separate Stochastic Gradient Descent (SGD) based
classifier has been used in order to differentiate among the finer classes
within that coarse class. TF-IDF representation of each word has been used as
feature for the SGD classifiers implemented as part of second stage
classification. Experiments show the effectiveness of our proposed method for
Bengali question classification
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