scholarly journals Urdu Sentiment Analysis via Multimodal Data Mining Based on Deep Learning Algorithms

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 153072-153082
Author(s):  
Urooba Sehar ◽  
Summrina Kanwal ◽  
Kia Dashtipur ◽  
Usama Mir ◽  
Ubaid Abbasi ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Multimodal Data ◽  
Multimodal Data Mining

scholarly journals To Enhance the Impact of Deep Learning-Based Algorithms in Determining the Behavior of an Individual based on Communication on Social Media

2019 ◽  
Vol 8 (12) ◽  
pp. 4433-4435
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Social Networking Sites ◽  
Opinion Mining ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Social Media Text ◽  
The Impact

In this digitized world, the Internet has become a prominent source to glean various kinds of information. In today’s scenario, people prefer virtual reality instead of one to one communication. The Majority of the population prefers social networking sites to voice themselves through posts, blogs, comments, likes, dislikes. Their sentiments can be found/traced using opinion mining or Sentiment analysis. Sentiment analysis of social media text is a useful technique for identifying peoples’ positive, negative or neutral emotions/sentiments/opinions. Sentiment analysis has gained special attention by researchers from last few years. Traditionally many machine learning algorithms were used to implement it like navie bays, Support Vector Machine and many more. But to overcome the drawbacks of ML in terms of complex classification algorithms different deep learning-based algorithms are introduced like CNN, RNN, and HNN. In this paper, we have studied different deep learning algorithms and intended to propose a deep learning-based model to analyze the behavior of an individual using social media text. Results given by the proposed model can utilize in a range of different fields like business, education, industry, politics, psychology, security, etc.

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

2011 ◽  
pp. 170-193
Author(s):  
Zhen Guo ◽  
Christos Faloutsos ◽  
Zhongfei (Mark) Zhang ◽  
Zhongfei (Mark) Zhang
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Learning Framework ◽  
New Information ◽  
Database Indexing ◽  
Constant Operation ◽  
Multimodal Data Mining

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.

A Highly Scalable and Adaptable Co-Learning Framework on Multimodal Data Mining in a Multimedia Database

Data Mining ◽  
2013 ◽  
pp. 567-586
Author(s):  
Zhongfei (Mark) Zhang ◽  
Zhen Guo ◽  
Christos Faloutsos ◽  
Jia-Yu Pan
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Multiple Instance Learning ◽  
Multimedia Database ◽  
Multimodal Data ◽  
Learning Framework ◽  
New Information ◽  
Database Indexing ◽  
Constant Operation ◽  
Multimodal Data Mining

This chapter presents a highly scalable and adaptable co-learning framework on multimodal data mining in a multimedia database. The co-learning framework is based on the multiple instance learning theory. The framework enjoys a strong scalability in the sense that the query time complexity is a constant, independent of the database scale, and the mining effectiveness is also independent of the database scale, allowing facilitating a multimodal querying to a very large scale multimedia database. At the same time, this framework also enjoys a strong adaptability in the sense that it allows incrementally updating the database indexing with a constant operation when the database is dynamically updated with new information. Hence, this framework excels many of the existing multimodal data mining methods in the literature that are neither scalable nor adaptable at all. Theoretic analysis and empirical evaluations are provided to demonstrate the advantage of the strong scalability and adaptability. While this framework is general for multimodal data mining in any specific domains, to evaluate this framework, the authors apply it to the Berkeley Drosophila ISH embryo image database for the evaluations of the mining performance. They have compared the framework with a state-of-the-art multimodal data mining method to demonstrate the effectiveness and the promise of the framework.

scholarly journals Performance Study of N-grams in the Analysis of Sentiments

2021 ◽  
pp. 477-483
Author(s):  
O. E. Ojo ◽  
A. Gelbukh ◽  
H. Calvo ◽  
O. O. Adebanji
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Performance Study ◽  
Analysis Techniques ◽  
Learning Techniques ◽  
Economic Texts ◽  
Economic Domain

In this work, a study investigation was carried out using n-grams to classify sentiments with different machine learning and deep learning methods. We used this approach, which combines existing techniques, with the problem of predicting sequence tags to understand the advantages and problems confronted with using unigrams, bigrams and trigrams to analyse economic texts. Our study aims to fill the gap by evaluating the performance of these n-grams features on different texts in the economic domain using nine sentiment analysis techniques and found more insights. We show that by comparing the performance of these features on different datasets and using multiple learning techniques, we extracted useful intelligence. The evaluation involves assessing the precision, recall, f1-score and accuracy of the function output of the several machine learning algorithms proposed. The methods were tested using Amazon, IMDB, Reuters, and Yelp economic review datasets and our comprehensive experiment shows the effectiveness of n-grams in the analysis of sentiments.

scholarly journals Rainfall Prediction using Machine Learning and Deep Learning Algorithms

2021 ◽  
Vol 10 (4) ◽  
pp. 251-254
Author(s):  
B.Meena Preethi ◽  
◽  
R. Gowtham ◽  
S. Aishvarya ◽  
S. Karthick ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Deep Learning ◽  
Missing Values ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Gradient Boosting ◽  
Rainfall Prediction ◽  
The Neural Network

The project entitled as “Rainfall Prediction using Machine Learning & Deep Learning Algorithms” is a research project which is developed in Python Language and dataset is stored in Microsoft Excel. This prediction uses various machine learning and deep learning algorithms to find which algorithm predicts with most accurately. Rainfall prediction can be achieved by using binary classification under Data Mining. Predicting the rainfall is very important in several aspects of one’s country and can help from preventing serious natural disasters. For this prediction, Artificial Neural Network using Forward and Backward Propagation, Ada Boost, Gradient Boosting and XGBoost algorithms are used in this model for predicting the rainfall. There are totally five modules used in this project. The Data Analysis Module will analyse the datasets and finding the missing values in the dataset. The Data Pre-processing includes Data Cleaning which is the process of filling the missing values in the dataset. The Feature Transformation Module is used to modify the features of the dataset. The Data Mining Module is used to train the dataset to models using any algorithm for learning the pattern. The Model Evaluation Module is used to measure the performance of the model and finalize the overall best accuracy for the prediction. Dataset used in this prediction is for the country Australia. This main aim of the project is to compare the various boosting algorithms with the neural network and find the best algorithm among them. This prediction can be major advantage to the farmers in order to plant the types of crops according to the needy of water. Overall, we analyse the algorithm which is feasible for qualitatively predicting the rainfall.

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