Verbal aggression detection on Twitter comments: convolutional neural network for short-text sentiment analysis

2018 ◽  
Vol 32 (15) ◽  
pp. 10809-10818 ◽  
Author(s):  
Junyi Chen ◽  
Shankai Yan ◽  
Ka-Chun Wong
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Verbal Aggression ◽  
Short Text ◽  
Text Sentiment Analysis

A fuzzy convolutional neural network for text sentiment analysis

2018 ◽  
Vol 35 (6) ◽  
pp. 6025-6034 ◽  
Author(s):  
Tuan-Linh Nguyen ◽  
Swathi Kavuri ◽  
Minho Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Text Sentiment Analysis

scholarly journals Research on Text Sentiment Analysis Based on Neural Network and Ensemble Learning

2021 ◽  
Vol 35 (1) ◽  
pp. 63-70
Author(s):  
Siyin Luo ◽  
Youjian Gu ◽  
Xingxing Yao ◽  
Wei Fan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Ensemble Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Text Sentiment Analysis

In view of the fact that a single sentiment classification model may be unstable in classification, this paper attempts to propose a joint neural network and ensemble learning sentiment analysis method. After data preprocessing such as word segmentation on the text, combined with document vectorization method for feature extraction, we then use four basic classifiers including long short-term memory network, convolutional neural network, a serial model combining convolutional neural network and long short-term memory network, and support vector machine to train model, respectively. Finally, the integration is carried out by stacking ensemble learning. The experimental results show that the integrated model significantly improves the accuracy of text sentiment analysis and it can effectively predict the sentiment polarity of the text.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

scholarly journals Combination of Convolutional Neural Network and Gated Recurrent Unit for Aspect-Based Sentiment Analysis

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 15561-15569
Author(s):  
Narisa Zhao ◽  
Huan Gao ◽  
Xin Wen ◽  
Hui Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Gated Recurrent Unit

scholarly journals Sentiment analysis in non-fixed length audios using a Fully Convolutional Neural Network

2021 ◽  
Vol 69 ◽  
pp. 102946
Author(s):  
María Teresa García-Ordás ◽  
Héctor Alaiz-Moretón ◽  
José Alberto Benítez-Andrades ◽  
Isaías García-Rodríguez ◽  
Oscar García-Olalla ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Fixed Length
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