Optimization Assisted Convolutional Neural Network for Sentiment Analysis with Weighted Holoentropy-based Features

2021 ◽  
pp. 1-37
Author(s):  
Hema Krishnan ◽  
M. Sudheep Elayidom ◽  
T. Santhanakrishnan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Semantic Similarity ◽  
Opinion Mining ◽  
Research Work ◽  
Constant Term ◽  
Batch Size ◽  
Major Work

Analyzing and gathering the people’s reactions on product trading, public services, etc. are crucial. Sentiment analysis (also termed as opinion mining) is a usual dialogue preparing act that plans on discovering the sentiments after opinions in texts on changing subjects. This research work adopts a novel sentiment analysis approach that comprises six phases like (i) Pre-processing, (ii) Keyword extraction and its sentiment categorization, (iii) Semantic word extraction, (iv) Semantic similarity checking, (v) Feature extraction, and (vi) Classification. Accordingly, the Mongodb documented tweets initially underwent pre-processing with stop word removal, stemming, and blank space removal. Regarding the extracted keywords, the existing semantic words are derived after categorizing the sentiment of keywords. Additionally, the semantic similarity score is evaluated along with their keywords. The subsequent step is feature extraction, where the Holoentropy features such as cross Holoentropy and joint Holoentropy are formulated. Along with this, the extraction of weighted holoentropy features is the major work, where weight is multiplied with the holoentropy features. Moreover, in order to enhance the performance of classification results, the constant term utilized in evaluating the weight function is optimized. For this optimal tuning, a new, improved algorithm termed as Self Adaptive Moth Flame Optimization (SA-MFO) is introduced, which is the adaptive version of MFO algorithm. For classification, this paper aims to use the Deep Convolutional Neural network (DCNN), where the batch size is fine-tuned using the same SA-MFO algorithm. Finally, the performance of the proposed work is compared over other conventional models with respect to different performance measures.

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.

An Improved Cross-Domain Sentiment Analysis Based on a Semi-Supervised Convolutional Neural Network

2022 ◽  
pp. 155-170
Author(s):  
Lap-Kei Lee ◽  
Kwok Tai Chui ◽  
Jingjing Wang ◽  
Yin-Chun Fung ◽  
Zhanhui Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Research Work ◽  
Latent Semantic Indexing ◽  
Future Research ◽  
Training Time ◽  
Cross Domain ◽  
Document Frequency

The dependence on Internet in our daily life is ever-growing, which provides opportunity to discover valuable and subjective information using advanced techniques such as natural language processing and artificial intelligence. In this chapter, the research focus is a convolutional neural network for three-class (positive, neutral, and negative) cross-domain sentiment analysis. The model is enhanced in two-fold. First, a similarity label method facilitates the management between the source and target domains to generate more labelled data. Second, term frequency-inverse document frequency (TF-IDF) and latent semantic indexing (LSI) are employed to compute the similarity between source and target domains. Performance evaluation is conducted using three datasets, beauty reviews, toys reviews, and phone reviews. The proposed method enhances the accuracy by 4.3-7.6% and reduces the training time by 50%. The limitations of the research work have been discussed, which serve as the rationales of future research directions.

scholarly journals Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

2021 ◽  
Vol 50 (2) ◽  
pp. 342-356
Author(s):  
Venkatesan Rajinikanth ◽  
Seifedine Kadry ◽  
Yunyoung Nam
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Brain Mri ◽  
Research Work ◽  
Disease Diagnosis ◽  
Detection Accuracy ◽  
Tumour Segmentation ◽  
Deep Feature Extraction

Due to the increased disease occurrence rates in humans, the need for the Automated Disease Diagnosis (ADD) systems is also raised. Most of the ADD systems are proposed to support the doctor during the screening and decision making process. This research aims at developing a Computer Aided Disease Diagnosis (CADD) scheme to categorize the brain tumour of 2D MRI slices into Glioblastoma/Glioma class with better accuracy. The main contribution of this research work is to develop a CADD system with Convolutional-Neural-Network (CNN) supported segmentation and classification. The proposed CADD framework consist of the following phases; (i) Image collection and resizing, (ii) Automated tumour segmentation using VGG-UNet, (iv) Deep-feature extraction using VGG16 network, (v) Handcrafted feature extraction, (vi) Finest feature choice by firefly-algorithm, and (vii) Serial feature concatenation and binary classification. The merit of the executed CADD is confirmed using an investigation realized using the benchmark as well as clinically collected brain MRI slices. In this work, a binary classification with a 10-fold cross validation is implemented using well known classifiers and the results attained with the SVM-Cubic (accuracy >98%) is superior. This result confirms that the combination of CNN assisted segmentation and classification helps to achieve enhanced disease detection accuracy.

Deep convolutional neural networks for human movement detection using wireless signals

2021 ◽  
pp. 1-10
Author(s):  
Chien-Cheng Leea ◽  
Zhongjian Gao ◽  
Xiu-Chi Huanga
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Human Detection ◽  
Two Dimensional ◽  
Dimensional Matrix ◽  
State Classification ◽  
Propagation Paths

This paper proposes a Wi-Fi-based indoor human detection system using a deep convolutional neural network. The system detects different human states in various situations, including different environments and propagation paths. The main improvements proposed by the system is that there is no cameras overhead and no sensors are mounted. This system captures useful amplitude information from the channel state information and converts this information into an image-like two-dimensional matrix. Next, the two-dimensional matrix is used as an input to a deep convolutional neural network (CNN) to distinguish human states. In this work, a deep residual network (ResNet) architecture is used to perform human state classification with hierarchical topological feature extraction. Several combinations of datasets for different environments and propagation paths are used in this study. ResNet’s powerful inference simplifies feature extraction and improves the accuracy of human state classification. The experimental results show that the fine-tuned ResNet-18 model has good performance in indoor human detection, including people not present, people still, and people moving. Compared with traditional machine learning using handcrafted features, this method is simple and effective.

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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