Semantic - based service discovery in grid environment

Shang, Kun

doi:10.3233/jifs-189011

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…To upgrade web administrations he offers a bunching based semantic assistance choice model and client special model. Shang et al [17] proposes a semantic layered coordinating calculation. To confirm the attainability and viability of the various leveled coordinating calculation dependent on linguistics, a model framework named guard stage milestone was planned.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic Analysis of Urdu English Tweets Empowered by Machine Learning

Tabassum¹,

Alyas²,

Hamid³

et al. 2021

Intelligent Automation &Amp; Soft Computing

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Development in the field of opinion mining and sentiment analysis has been rapid and aims to explore views or texts on various social media sites through machine-learning techniques with the sentiment, subjectivity analysis and calculations of polarity. Sentiment analysis is a natural language processing strategy used to decide if the information is positive, negative, or neutral and it is frequently performed on literature information to help organizations screen brand, item sentiment in client input, and comprehend client needs. In this paper, two strategies for sentiment analysis is proposed for word embedding and a bag of words on Urdu and English tweets. Word embedding is a notable arrangement of procedures that can remember words linguistics dependent on the spread theory which expresses that word is utilized and happens within the same settings tend to indicate comparable implications. Bag of words is an approach used in natural language processing to retrieve information and features from written documents. For the bag of words, machine learning techniques like naive bayes, decision tree, k-nearest neighbor, and support vector machine is used to enhance the accuracy. For word embedding the neural network technique is proposed by the combination of recurrent neural network (RNN) with long-short term memory (LSTM) for sentimental analysis of tweets. Datasets of Urdu and English tweets are used for negative and positive classification tweets with machine learning techniques. The contribution of this paper involves the implementation of a hybrid approach that focused on a sentiment analyzer to overcome social network challenges and also provided the comparative analysis of different machine learning algorithms. The results indicate improvement while using the combination of RNN with the help of LSTM showed accuracy 87% on the Urdu dataset and 92% on the English dataset.

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Section: Literature Reviewmentioning

confidence: 99%