Deep Learning Techniques on Text Classification Using Natural Language Processing (NLP) In Social Healthcare Network: A Comprehensive Survey

2021 ◽  
Author(s):  
PM. Lavanya ◽  
E. Sasikala
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Text Classification ◽  
Healthcare Network ◽  
Learning Techniques ◽  
Comprehensive Survey

scholarly journals Application of Natural Language Processing on Character Computing : A Short Review

2021 ◽  
Author(s):  
KOUSHIK DEB
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Personality Trait ◽  
Prediction Models ◽  
Short Review ◽  
Learning Techniques ◽  
Trait Prediction

Character Computing consists of not only personality trait recognition, but also correlation among these traits. Tons of research has been conducted in this area. Various factors like demographics, sentiment, gender, LIWC, and others have been taken into account in order to understand human personality. In this paper, we have concentrated on the factors that could be obtained from available data using Natural Language Processing. It has been observed that the most successful personality trait prediction models are highly dependent on NLP techniques. Researchers across the globe have used different kinds of machine learning and deep learning techniques to automate this process. Different combinations of factors lead the research in different directions. We have presented a comparative study among those experiments and tried to derive a direction for future development.

Deep Learning Approaches for Textual Sentiment Analysis

2020 ◽  
pp. 171-182 ◽  
Author(s):  
Tamanna Sharma ◽  
Anu Bajaj ◽  
Om Prakash Sangwan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Computational Technique ◽  
Learning Techniques

Sentiment analysis is computational measurement of attitude, opinions, and emotions (like positive/negative) with the help of text mining and natural language processing of words and phrases. Incorporation of machine learning techniques with natural language processing helps in analysing and predicting the sentiments in more precise manner. But sometimes, machine learning techniques are incapable in predicting sentiments due to unavailability of labelled data. To overcome this problem, an advanced computational technique called deep learning comes into play. This chapter highlights latest studies regarding use of deep learning techniques like convolutional neural network, recurrent neural network, etc. in sentiment analysis.

scholarly journals A Deep Insight in Challenges of Natural Language Processing and Usage of Deep Learning

2020 ◽  
Vol 8 (12s3) ◽  
pp. 50-54
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Automatic Speech Recognition ◽  
Data Driven ◽  
Deep Insight ◽  
Pos Tagging ◽  
Learning Techniques ◽  
Using Data

Natural Language Processing (NLP) using the power of artificial intelligence has empowered the understanding of the language used by human. It has also enhanced the effectiveness of the communication between human and computers. The complexity and diversity of the huge datasets have raised the requirement for automatic analysis of the linguistic data by using data-driven approaches. The performance of the data-driven approaches is improved after the usage of different deep learning techniques in various application areas of NLP like Automatic Speech Recognition, POS tagging etc. The paper addresses the challenges faced in NLP and the use of deep learning techniques in different application areas of NLP.

scholarly journals Fake News Detector in Online Social Media

2019 ◽  
Vol 9 (1S4) ◽  
pp. 58-60
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Fake News ◽  
The Public ◽  
Online Social Media ◽  
Learning Techniques ◽  
State Of Art

Spreading of fake news in online social media is a major nuisance to the public and there is no state of art tool to detect whether a news is a fake or an original one in an automated manner. Hence, this paper analyses the online social media and the news feeds for detection of fake news. The work proposes solution using Natural Language Processing and Deep Learning techniques for detecting the fake news in online social media.

scholarly journals Deep Learning based NLP Techniques In Text to Speech Synthesis for Communication Recognition

2020 ◽  
Vol 2 (4) ◽  
pp. 209-215
Author(s):  
Eriss Eisa Babikir Adam
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Speech Synthesis ◽  
Large Scale ◽  
Feature Representations ◽  
Large Scale Data ◽  
Learning Techniques ◽  
Text To Speech Synthesis

The computer system is developing the model for speech synthesis of various aspects for natural language processing. The speech synthesis explores by articulatory, formant and concatenate synthesis. These techniques lead more aperiodic distortion and give exponentially increasing error rate during process of the system. Recently, advances on speech synthesis are tremendously moves towards deep learning process in order to achieve better performance. Due to leverage of large scale data gives effective feature representations to speech synthesis. The main objective of this research article is that implements deep learning techniques into speech synthesis and compares the performance in terms of aperiodic distortion with prior model of algorithms in natural language processing.

Speech-Act Classification Using Convolutional Neural Network and Word Embedding

2018 ◽  
Vol 27 (06) ◽  
pp. 1850026
Author(s):  
Kyoungman Bae ◽  
Youngjoong Ko
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Convolutional Neural Network ◽  
Language Processing ◽  
Speech Acts ◽  
Speech Act ◽  
Distributed Representation ◽  
Learning Techniques

The application of deep learning techniques in natural language processing tasks has been increased in recent years. Many studies have used the deep learning techniques to obtain a distributed representation of features. In particular, the convolutional neural network (CNN) with the distributed representation have subsequently been shown to be effective for the natural language processing tasks. This paper presents how to apply the CNN to speech-act classification. Then we analyze the experimental results on two issues, how to solve two problems about sparse speech-acts in train data and out of vocabulary, and how to utilize the advantages of CNN in the speech-act classification. As a result, we obtain the significant improved performances when CNN is applied to the speech-act classification.

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