scholarly journals An Ensemble Classification System for Twitter Sentiment Analysis

2018 ◽  
Vol 132 ◽  
pp. 937-946 ◽  
Author(s):  
Ankit ◽  
Nabizath Saleena
Keyword(s):  
Sentiment Analysis ◽  
Classification System ◽  
Ensemble Classification

Ensemble Classification System for Scientific Chart Recognition from PDF Files

2012 ◽  
Vol 2 (4) ◽  
pp. 1-10 ◽  
Author(s):  
S. Nagarajan ◽  
V. Karthikeyani
Keyword(s):  
Feature Extraction ◽  
Classification System ◽  
Texture Features ◽  
Portable Document Format ◽  
Ensemble Classification ◽  
3 Dimensional ◽  
Feature Based ◽  
Separation Feature ◽  
Object Features ◽  
Document Format

Portable Document Format (PDF) is the most frequently used universal document format on the Internet and E-Publishing. Wide usage of PDF files has increased the need of conversion tools that convert PDF file content to text or HTML formats. A PDF converter can be categorized into two domains, namely, text recognition and graphics recognition. This paper focus on graphic recognition, especially chart type identification, which is concerned with developing algorithms that has the ability to determine the type of a given chart image from a PDF file. In the proposed system, initially an enhanced connected component and statistical feature based method is used to separate the chart region from other regions. The chart region is then analyzed and grouped as either 2-dimensional or 3-dimensional chart. After separating the graphic component from the text components, feature extraction is performed. The features can be grouped as object features, texture features and shape features. The combined feature vector is then classified using ensemble classification system. Experimental results show that the chart separation, feature extraction and ensemble classification models significantly improve the quality of chart identification.

scholarly journals Ensemble Malware Classification System Using Deep Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 721 ◽  
Author(s):  
Barath Narayanan Narayanan ◽  
Venkata Salini Priyamvada Davuluru
Keyword(s):  
Neural Networks ◽  
Classification System ◽  
Short Term Memory ◽  
Ensemble Classification ◽  
Machine Language ◽  
Support Vector ◽  
Automated Classification ◽  
Malware Classification ◽  
Lstm Network ◽  
Computational Resources

With the advancement of technology, there is a growing need of classifying malware programs that could potentially harm any computer system and/or smaller devices. In this research, an ensemble classification system comprising convolutional and recurrent neural networks is proposed to distinguish malware programs. Microsoft’s Malware Classification Challenge (BIG 2015) dataset with nine distinct classes is utilized for this study. This dataset contains an assembly file and a compiled file for each malware program. Compiled files are visualized as images and are classified using Convolutional Neural Networks (CNNs). Assembly files consist of machine language opcodes that are distinguished among classes using Long Short-Term Memory (LSTM) networks after converting them into sequences. In addition, features are extracted from these architectures (CNNs and LSTM) and are classified using a support vector machine or logistic regression. An accuracy of 97.2% is achieved using LSTM network for distinguishing assembly files, 99.4% using CNN architecture for classifying compiled files and an overall accuracy of 99.8% using the proposed ensemble approach thereby setting a new benchmark. An independent and automated classification system for assembly and/or compiled files provides the luxury to anti-malware industry experts to choose the type of system depending on their available computational resources.

scholarly journals Sentiment Classification System of Twitter Data using Python

2020 ◽  
Vol 8 (6) ◽  
pp. 1042-1044
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Real Time ◽  
Classification System ◽  
Sentiment Classification ◽  
Time Data ◽  
Twitter Data ◽  
Real Time Data ◽  
Share Data ◽  
Day By Day

Social media has developed drastically over the years. These days, individuals from all around the globe utilize online networking destinations to share data and information. Twitter is a well known communication site where users update information or messages known as tweets. Users share their day by day lives, post their opinions on everything, for example, brands and places. Various purchasers and advertisers utilize these tweets to accumulate bits of knowledge of their items and opinions on them. The aim of this paper is to exhibit a model that can perform sentiment analysis of real-time data collected from twitter and classify the tweets into positive, negative or neutral based on the sentiment expressed in them.

Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis

2021 ◽  
pp. 16-28
Author(s):  
Ishrat Nazeer ◽  
Mamoon Rashid ◽  
Sachin Kumar Gupta ◽  
Abhishek Kumar
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Training Model ◽  
Ensemble Classifier ◽  
Machine Learning Algorithms ◽  
Ensemble Classification ◽  
The Novel ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach ◽  
Media Sentiment

Twitter is a platform where people express their opinions and come with regular updates. At present, it has become a source for many organizations where data will be extracted and then later analyzed for sentiments. Many machine learning algorithms are available for twitter sentiment analysis which are used for automatically predicting the sentiment of tweets. However, there are challenges that hinder machine learning classifiers to achieve better results in terms of classification. In this chapter, the authors are proposing a novel feature generation technique to provide desired features for training model. Next, the novel ensemble classification system is proposed for identifying sentiment in tweets through weighted majority rule ensemble classifier, which utilizes several commonly used statistical models like naive Bayes, random forest, logistic regression, which are weighted according to their performance on historical data, where weights are chosen separately for each model.

scholarly journals Comprehensive Study on Lexicon-based Ensemble Classification Sentiment Analysis

Entropy ◽  
2015 ◽  
Vol 18 (1) ◽  
pp. 4 ◽  
Author(s):  
Łukasz Augustyniak ◽  
Piotr Szymański ◽  
Tomasz Kajdanowicz ◽  
Włodzimierz Tuligłowicz
Keyword(s):  
Sentiment Analysis ◽  
Ensemble Classification ◽  
Comprehensive Study
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