Sentiment Analysis Using Deep Learning Approach

Cen, Peng; Zhang, Kexin; Zheng, Dongqi

doi:10.32604/jai.2020.010132

Cited by 26 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In another study, [6] used deep learning approaches to analyze sentiments from IMDB reviews. The classification of reviews is necessary, for researchers, it can be based on the relevance of the sentiment and ratings of the film.…”

Section: Supervised Learning Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Towards a user-oriented adaptive system based on sentiment analysis from text

Baqach

Battou

2021

E3S Web Conf.

View full text Add to dashboard Cite

Sentiment analysis has known a big interest over recent years due to the expansion of data. It has many applications in different fields such as marketing, psychology, human-computer interaction, eLearning, etc. There are many forms of sentiment analysis, namely facial expressions, speech, and text. This article is more interested in sentiment analysis from the text as it is a relatively new field and still needs more effort and research. Sentiment analysis from text is very important for different fields, for eLearning it can be critical in determining the emotional state of students and therefore, putting in place the necessary interactions to motivate students to engage and complete their courses. In this article, we present different methods of sentiment analysis from the text that exist in the literature, beginning from the selection of features or text representation, until the training of the prediction model using either supervised or unsupervised learning algorithms and although there has been so much work done in this domain, there is still effort that can be done to improve the performance and to do that we first need to review the recent methods and approaches put in place on this field and then try to discuss improvements in certain approaches or even proposing new approaches.

show abstract

Section: Supervised Learning Methodsmentioning

confidence: 99%

“…One-hot encoding is used in many studies of text classification, [6] used this approach after pre-processing the text, and then fed the resulted matrix into different deep learning methods to compare them.…”

Section: Introductionmentioning

confidence: 99%

Towards a user-oriented adaptive system based on sentiment analysis from text

Baqach

Battou

2021

E3S Web Conf.

View full text Add to dashboard Cite

show abstract

“…The hybrid approach of analyzing sentiments consisting the statistical and knowledge-based methods to recognize the polarity [37]. Most novel researchers are using a hybrid approach to sentiment analysis due to the reason that this method can enhance the accuracy of the sentiment analyzing model [38,39,40].…”

Section: Hybrid Approachmentioning

confidence: 99%

“…Recurrent Neural Network is using sequence data as the input and it is generated by applying a similar set of weights continuously or recursively on that sequence of input data [38]. This is helps to analyze the deep structured information and it is integrally a complex network.…”

Section: Recursive Neural Network (Rnn)mentioning

confidence: 99%

Literature review on Real-time Location-Based Sentiment Analysis on Twitter

Rathnayaka¹,

Jayasena²,

Ratnayake³

2021

Adv. Technol.

View full text Add to dashboard Cite

Sentiment analysis mainly supports sorting out the polarity and provides valuable information with the use of raw data in social media platforms. Many fields like health, business, and security require real-time data analysis for instant decision-making situations.Since Twitter is considered a popular social media platform to collect data easily, this paper is considering data analysis methods of Twitter data, real-time Twitter data analysis based on geo-location. Twitter data classification and analysis can be done with the use of diverse algorithms and deciding the most appropriate algorithm for data analysis, can be accomplished by implementing and testing these diverse algorithms.This paper is discussing the major description of sentiment analysis, data collection methods, data pre-processing, feature extraction, and sentiment analysis methods related to Twitter data. Real-time data analysis arises as a major method of analyzing the data available online and the real-time Twitter data analysis process is described throughout this paper. Several methods of classifying the polarized Twitter data are discussed within the paper while depicting a proposed method of Twitter data analyzing algorithm. Location-based Twitter data analysis is another crucial aspect of sentiment analyses, that enables data sorting according to geo-location, and this paper describes the way of analyzing Twitter data based on geo-location. Further, a comparison about several sentiment analysis algorithms used by previous researchers has been reported and finally, a conclusion has been provided.

show abstract

“…This kind of method merges connected multiple-feature , θ and β (β w and β r ), we need to infer the posterior distribution of the parameters z and l, which means finding the word distribution for the topic and sentiment labels. Here, we use the external datasets MDS [48], Subject MR [49], and Review Text Content [50] as prior knowledge. Let P (z, l) be the sampling distribution of the words given the remaining topics and sentimental labels.…”

Section: Introductionmentioning

confidence: 99%

Joint Sentiment Part Topic Regression Model for Multimodal Analysis

Zhu

Gao

et al. 2020

Information

View full text Add to dashboard Cite

The development of multimodal media compensates for the lack of information expression in a single modality and thus gradually becomes the main carrier of sentiment. In this situation, automatic assessment for sentiment information in multimodal contents is of increasing importance for many applications. To achieve this, we propose a joint sentiment part topic regression model (JSP) based on latent Dirichlet allocation (LDA), with a sentiment part, which effectively utilizes the complementary information between the modalities and strengthens the relationship between the sentiment layer and multimodal content. Specifically, a linear regression module is developed to share implicit variables between image–text pairs, so that one modality can predict the other. Moreover, a sentiment label layer is added to model the relationship between sentiment distribution parameters and multimodal contents. Experimental results on several datasets verify the feasibility of our proposed approach for multimodal sentiment analysis.

show abstract

Sentiment Analysis Using Deep Learning Approach

Cited by 26 publications

References 16 publications

Towards a user-oriented adaptive system based on sentiment analysis from text

Towards a user-oriented adaptive system based on sentiment analysis from text

Literature review on Real-time Location-Based Sentiment Analysis on Twitter

Joint Sentiment Part Topic Regression Model for Multimodal Analysis

Contact Info

Product

Resources

About