Sentiment analysis for Chinese microblog based on deep neural networks with convolutional extension features

2016 ◽  
Vol 210 ◽  
pp. 227-236 ◽  
Author(s):  
Xiao Sun ◽  
Chengcheng Li ◽  
Fuji Ren
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks

A Unified Framework for Improving Misclassifications in Modern Deep Neural Networks for Sentiment Analysis

2021 ◽  
Author(s):  
Ahoud Alhazmi ◽  
Abdulwahab Aljubairy ◽  
Wei Emma Zhang ◽  
Quan Z Sheng ◽  
Elaf Alhazmi
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Unified Framework

scholarly journals A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks

2020 ◽  
Vol 380 ◽  
pp. 1-10 ◽  
Author(s):  
Kia Dashtipour ◽  
Mandar Gogate ◽  
Jingpeng Li ◽  
Fengling Jiang ◽  
Bin Kong ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Analysis Framework ◽  
Dependency Grammar

Multimodal Sentiment Analysis Using Deep Neural Networks

2017 ◽  
pp. 58-65 ◽  
Author(s):  
Harika Abburi ◽  
Rajendra Prasath ◽  
Manish Shrivastava ◽  
Suryakanth V. Gangashetty
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Multimodal Sentiment Analysis

scholarly journals Lexical Variation and Sentiment Analysis of Roman Urdu Sentences with Deep Neural Networks

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Muhammad Arslan Manzoor ◽  
Saqib Mamoon ◽  
Song Kei ◽  
Ali Zakir ◽  
Muhammad Adil ◽  
...  
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Lexical Variation

scholarly journals Sentiment Analysis of Short Russian Texts Using BERT and Word2Vec Embeddings

2021 ◽  
Author(s):  
Ekaterina Popova ◽  
Vladimir Spitsyn
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Social Networks ◽  
Sentiment Analysis ◽  
Deep Neural Networks ◽  
Functional Method ◽  
Fine Tuning ◽  
Machine Learning Techniques ◽  
Word Embeddings ◽  
Learning Techniques

This article is devoted to modern approaches for sentiment analysis of short Russian texts from social networks using deep neural networks. Sentiment analysis is the process of detecting, extracting, and classifying opinions, sentiments, and attitudes concerning different topics expressed in texts. The importance of this topic is linked to the growth and popularity of social networks, online recommendation services, news portals, and blogs, all of which contain a significant number of people's opinions on a variety of topics. In this paper, we propose machine-learning techniques with BERT and Word2Vec embeddings for tweets sentiment analysis. Two approaches were explored: (a) a method, of word embeddings extraction and using the DNN classifier; (b) refinement of the pre-trained BERT model. As a result, the fine- tuning BERT outperformed the functional method to solving the problem.

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