Location-Based Sentiment Analysis of the Revocation of Article 370 Using Various Recurrent Neural Networks

2021 ◽  
pp. 45-55
Author(s):  
Abhineeth Mishra ◽  
Arti Arya ◽  
H. R. Devanand
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks

scholarly journals A Unified Model for Opinion Target Extraction and Target Sentiment Prediction

2019 ◽  
Vol 33 ◽  
pp. 6714-6721 ◽  
Author(s):  
Xin Li ◽  
Lidong Bing ◽  
Piji Li ◽  
Wai Lam
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Unified Model ◽  
Primary Target ◽  
Target Extraction ◽  
Final Output ◽  
Benchmark Datasets ◽  
Task Dependency ◽  
Current Word

Target-based sentiment analysis involves opinion target extraction and target sentiment classification. However, most of the existing works usually studied one of these two sub-tasks alone, which hinders their practical use. This paper aims to solve the complete task of target-based sentiment analysis in an end-to-end fashion, and presents a novel unified model which applies a unified tagging scheme. Our framework involves two stacked recurrent neural networks: The upper one predicts the unified tags to produce the final output results of the primary target-based sentiment analysis; The lower one performs an auxiliary target boundary prediction aiming at guiding the upper network to improve the performance of the primary task. To explore the inter-task dependency, we propose to explicitly model the constrained transitions from target boundaries to target sentiment polarities. We also propose to maintain the sentiment consistency within an opinion target via a gate mechanism which models the relation between the features for the current word and the previous word. We conduct extensive experiments on three benchmark datasets and our framework achieves consistently superior results.

Sentiment Analysis Using Gated Recurrent Neural Networks

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Sharat Sachin ◽  
Abha Tripathi ◽  
Navya Mahajan ◽  
Shivani Aggarwal ◽  
Preeti Nagrath
Keyword(s):  
Neural Networks ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks

Live Chat Analysis Using Machine Learning

2021 ◽  
Vol 8 (1) ◽  
pp. 39-44
Author(s):  
S. Kavibharathi ◽  
S. Lakshmi Priyankaa ◽  
M.S. Kaviya ◽  
Dr.S. Vasanthi
Keyword(s):  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Learning Model ◽  
Live Chat ◽  
Wide Range ◽  
Depth Analysis

The World Wide Web such as social networking sites and blog comments forum has huge user comments emotion data from different social events and product brand and arguments in the form of political views. Generate a heap. Reflects the user's mood on the network, the reader, has a huge impact on product suppliers and politicians. The challenge for the credibility of the analysis is the lack of sufficient tag data in the Natural Language Processing (NLP) field. Positive and negative classify content based on user feedback, live chat, whether the user is used as the base for a wide range of tasks related to the text content of a meaningful assessment. Data collection, and function number for all variants. A recurrent neural network is very good text classification. Analyzing unstructured form from social media data, reasonable structure, and analyzes attach great importance to note for this emotion. Emotional rewiring can use natural language processing sentiment analysis to predict. In the method by the Recurrent Neural Networks (RNNs) of the proposed prediction chat live chat into sentiment analysis. Sentiment analysis and in-depth learning technology have been integrated into the solution to this problem, with their deep learning model automatic learning function is active. Using a Recurrent Neural Networks (RNNs) reputation analysis to solve various problems and language problems of text analysis and visualization product retrospective sentiment classifier cross-depth analysis of the learning model implementation.

scholarly journals Design and Investigation of Capsule Networks for Sentence Classification

2019 ◽  
Vol 9 (11) ◽  
pp. 2200 ◽  
Author(s):  
Haftu Wedajo Fentaw ◽  
Tae-Hyong Kim
Keyword(s):  
Neural Networks ◽  
Image Analysis ◽  
Sentiment Analysis ◽  
Recurrent Neural Networks ◽  
Text Processing ◽  
Classification Performance ◽  
Sequential Order ◽  
Great Performance ◽  
Sentence Classification ◽  
Performance Gains

In recent years, convolutional neural networks (CNNs) have been used as an alternative to recurrent neural networks (RNNs) in text processing with promising results. In this paper, we investigated the newly introduced capsule networks (CapsNets), which are getting a lot of attention due to their great performance gains on image analysis more than CNNs, for sentence classification or sentiment analysis in some cases. The results of our experiment show that the proposed well-tuned CapsNet model can be a good, sometimes better and cheaper, substitute of models based on CNNs and RNNs used in sentence classification. In order to investigate whether CapsNets can learn the sequential order of words or not, we performed a number of experiments by reshuffling the test data. Our CapsNet model shows an overall better classification performance and better resistance to adversarial attacks than CNN and RNN models.

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