Application of Deep Learning to Sentiment Analysis for recommender system on cloud

Preethi, G.; Krishna, P. Venkata; Obaidat, Mohammad S.; Saritha, V.; Yenduri, Sumanth

doi:10.1109/cits.2017.8035341

Cited by 69 publications

(24 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Some other studies applied deep-learning-based sentiment analysis in different domains, including finance [5,6], weather-related tweets [10], trip advisors [11], recommender systems for cloud services [12], and movie reviews [13][14][15][16][17][18]. In [10], where text features were automatically extracted from different data sources, user information and weather knowledge were transferred into word embedding using the Word2vec tool.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

View full text Add to dashboard Cite

The study of public opinion can provide us with valuable information. The analysis of sentiment on social networks, such as Twitter or Facebook, has become a powerful means of learning about the users' opinions and has a wide range of applications. However, the efficiency and accuracy of sentiment analysis is being hindered by the challenges encountered in natural language processing (NLP). In recent years, it has been demonstrated that deep learning models are a promising solution to the challenges of NLP. This paper reviews the latest studies that have employed deep learning to solve sentiment analysis problems, such as sentiment polarity. Models using term frequency-inverse document frequency (TF-IDF) and word embedding have been applied to a series of datasets. Finally, a comparative study has been conducted on the experimental results obtained for the different models and input features.

show abstract

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis Based on Deep Learning: A Comparative Study

2020

View full text Add to dashboard Cite

show abstract

“…For an efficiently trained classifier for various issues, different feature vectors should be developed Different controlled classifiers with a similar feature vector are trained in [13] and variance of the correctness is registered Results showed that Naive Bayes gave better results as compared to…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

2021

IJATCSE

View full text Add to dashboard Cite

More and more individuals are now using online social networks and resources throughout this day and age to not only interact and to communicate but also for sharing their views, experiences, ideas, impression about anything. The analysis of sentiments is the identification and categorization of these views to evaluate public opinions on a specific subject, question, product, etc. Day by day, the relevance of sentiment analysis is growing up. Machine learning is an area or field of computer science where, without being specifically programmed, computers can learn. Deep learning is the part of machine learning and deals with the algorithm, which is most widely used as Neural network, neural belief, etc., in which neuronal implementations are considered. For sentiment analysis, it compares their performance and accuracy so then it can be inferred that deep learning techniques in most of the cases provide better results. The gap in the precision of these two approaches, however, is not as important enough in certain situations, and so it is best to apply and use the machine learning approaches and methods because these are simpler in terms of Implementation

show abstract

“…Spark uses a distributed memory computing platform, which can effectively support iterative computing. In addition, each node in the same layer RBM is independent of each other, which provides conditions for distributed computing of DBN model [17], [18]. Therefore, this paper uses Spark as the parallel framework of emotional classification model.…”

Section: B Parallel Optimization Of Emotional Classifier Based On Spmentioning

confidence: 99%

Research on Parallelization of Microblog Emotional Analysis Algorithms Using Deep Learning and Attention Model Based on Spark Platform

Shi

2019

IEEE Access

View full text Add to dashboard Cite

Emotional analysis of microblog can discover that the public's attitude towards hot events can grasp the network public opinion, so it has become a hot research topic in text mining. In view of the current situation that most of the existing affective analysis methods separate the deep learning model from the emotional symbols, this paper proposes a microblog affective analysis method based on dual attention model. This method uses Word2Vec tool to express microblog platform and build emotional dictionary; uses deep belief network and attention model to construct microblog emotional classification model; and trains the emotional classification model based on the constructed semantic representation. By combining attention model with emotional symbols, this method effectively enhances the ability of capturing emotional semantics of microblog text and improves the performance of microblog emotional classification. Based on Spark cluster, the deep confidence neural network is processed in parallel. The experimental results show that the model achieves the best results in three indicators. The recall and precision of the model are more than 0.04 higher than the traditional shallow learning method. INDEX TERMS Emotional analysis algorithms, deep learning, deep belief network (DBN), spark parallelization, text mining.

show abstract

Application of Deep Learning to Sentiment Analysis for recommender system on cloud

Cited by 69 publications

References 5 publications

Sentiment Analysis Based on Deep Learning: A Comparative Study

Sentiment Analysis Based on Deep Learning: A Comparative Study

Sentiment Analysis by using deep learning and Machine learning Techniques: A Review

Research on Parallelization of Microblog Emotional Analysis Algorithms Using Deep Learning and Attention Model Based on Spark Platform

Contact Info

Product

Resources

About