scholarly journals Sentiment Analysis for Roman Urdu

2019 ◽  
Vol 38 (2) ◽  
pp. 463-470 ◽  
Author(s):  
Ayesha Rafique ◽  
Kamran Malik ◽  
Zubair Nawaz ◽  
Faisal Bukhari ◽  
Akhtar Hussain Jalbani
Keyword(s):  
Sentiment Analysis ◽  
Gradient Descent ◽  
English Language ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Online Comments ◽  
Better Than

The majority of online comments/opinions are written in text-free format. Sentiment Analysis can be used as a measure to express the polarity (positive/negative) of comments/opinions. These comments/ opinions can be in different languages i.e. English, Urdu, Roman Urdu, Hindi, Arabic etc. Mostly, people have worked on the sentiment analysis of the English language. Very limited research work has been done in Urdu or Roman Urdu languages. Whereas, Hindi/Urdu is the third largest language in the world. In this paper, we focus on the sentiment analysis of comments/opinions in Roman Urdu. There is no publicly available Roman Urdu public opinion dataset. We prepare a dataset by taking comments/opinions of people in Roman Urdu from different websites. Three supervised machine learning algorithms namely NB (Naive Bayes), LRSGD (Logistic Regression with Stochastic Gradient Descent) and SVM (Support Vector Machine) have been applied on this dataset. From results of experiments, it can be concluded that SVM performs better than NB and LRSGD in terms of accuracy. In case of SVM, an accuracy of 87.22% is achieved.

scholarly journals Financial Context News Sentiment Analysis for the Lithuanian Language

2021 ◽  
Vol 11 (10) ◽  
pp. 4443
Author(s):  
Rokas Štrimaitis ◽  
Pavel Stefanovič ◽  
Simona Ramanauskaitė ◽  
Asta Slotkienė
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Experimental Investigations ◽  
Support Vector ◽  
Applied Machine Learning ◽  
Bayes Algorithm ◽  
Website Content

Financial area analysis is not limited to enterprise performance analysis. It is worth analyzing as wide an area as possible to obtain the full impression of a specific enterprise. News website content is a datum source that expresses the public’s opinion on enterprise operations, status, etc. Therefore, it is worth analyzing the news portal article text. Sentiment analysis in English texts and financial area texts exist, and are accurate, the complexity of Lithuanian language is mostly concentrated on sentiment analysis of comment texts, and does not provide high accuracy. Therefore in this paper, the supervised machine learning model was implemented to assign sentiment analysis on financial context news, gathered from Lithuanian language websites. The analysis was made using three commonly used classification algorithms in the field of sentiment analysis. The hyperparameters optimization using the grid search was performed to discover the best parameters of each classifier. All experimental investigations were made using the newly collected datasets from four Lithuanian news websites. The results of the applied machine learning algorithms show that the highest accuracy is obtained using a non-balanced dataset, via the multinomial Naive Bayes algorithm (71.1%). The other algorithm accuracies were slightly lower: a long short-term memory (71%), and a support vector machine (70.4%).

scholarly journals APPLICATION OF MACHINE LEARNING ALGORITHMS FOR PROCESSING COMMENTS FROM THE YOUTUBE VIDEO HOSTING UNDER TRAINING VIDEOS

2021 ◽  
pp. 33-42
Author(s):  
L. S. Koriashkina ◽  
H. V. Symonets
Keyword(s):  
Machine Learning ◽  
Gradient Descent ◽  
Russian Language ◽  
Stochastic Gradient ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Gradient Enhancement

Purpose. Detecting toxic comments on YouTube video hosting under training videos by classifying unstructured text using a combination of machine learning methods. Methodology. To work with the specified type of data, machine learning methods were used for cleaning, normalizing, and presenting textual data in a form acceptable for processing on a computer. Directly to classify comments as “toxic”, we used a logistic regression classifier, a linear support vector classification method without and with a learning method – stochastic gradient descent, a random forest classifier and a gradient enhancement classifier. In order to assess the work of the classifiers, the methods of calculating the matrix of errors, accuracy, completeness and F-measure were used. For a more generalized assessment, a cross-validation method was used. Python programming language. Findings. Based on the assessment indicators, the most optimal methods were selected – support vector machine (Linear SVM), without and with the training method using stochastic gradient descent. The described technologies can be used to analyze the textual comments under any training videos to detect toxic reviews. Also, the approach can be useful for identifying unwanted or even aggressive information on social networks or services where reviews are provided. Originality. It consists in a combination of methods for preprocessing a specific type of text, taking into account such features as the possibility of having a timecode, emoji, links, and the like, as well as in the adaptation of classification methods of machine learning for the analysis of Russian-language comments. Practical value. It is about optimizing (simplification) the comment analysis process. The need for this processing is due to the growing volumes of text data, especially in the field of education through quarantine conditions and the transition to distance learning. The volume of educational Internet content already needs to automate the processing and analysis of feedback, over time this need will only grow.

scholarly journals Analyzing Fake News Based on Machine Learning Algorithms

2020 ◽  
Author(s):  
Pawar A B ◽  
Jawale M A ◽  
Kyatanavar D N
Keyword(s):  
Language Processing ◽  
Gradient Descent ◽  
Human Life ◽  
Stochastic Gradient ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fake News ◽  
Processing Techniques

Usages of Natural Language Processing techniques in the field of detection of fake news is analyzed in this research paper. Fake news are misleading concepts spread by invalid resources can provide damages to human-life, society. To carry out this analysis work, dataset obtained from web resource OpenSources.co is used which is mainly part of Signal Media. The document frequency terms as TF-IDF of bi-grams used in correlation with PCFG (Probabilistic Context Free Grammar) on a set of 11,000 documents extracted as news articles. This set tested on classification algorithms namely SVM (Support Vector Machines), Stochastic Gradient Descent, Bounded Decision Trees, Gradient Boosting algorithm with Random Forests. In experimental analysis, found that combination of Stochastic Gradient Descent with TF-IDF of bi-grams gives an accuracy of 77.2% in detecting fake contents, which observes with PCFGs having slight recalling defects

scholarly journals Aspect Based Sentimental Analysis of Hotel Reviews: A Comparative Study

2020 ◽  
Vol 4 (1) ◽  
pp. 11-20
Keyword(s):  
Machine Learning ◽  
Comparative Study ◽  
Sentiment Analysis ◽  
Opinion Mining ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Practical Significance ◽  
Support Vector ◽  
Use Of The Internet ◽  
And Task

The increasing use of the internet enables users to share their opinion about what they like and dislike regarding products and services. For efficient decision making, there is a need to analyze these reviews. Sentiment analysis or opinion mining is commonly used to detect polarity (positive or negative) of reviews. But, it does not show the aspect or orientation of the text. In this study, state-of-art approaches based on supervised machine learning employed to perform three tasks on the dataset provided by SemEval. Tasks A and B are related to predicting the aspect of the restaurant’s reviews, whereas task C shows their polarity. Additionally, this study aims to compare the performance of two feature engineering techniques and five machine learning algorithms to evaluate their performance on a publicly available dataset named SemEval-2015 Task 12. The experimental results showed that the word2vec features when used with the support vector machine algorithm outperformed by giving 76%, 72% and 79% off overall accuracies for Task A, Task B, and Task C respectively. Our comparative study holds practical significance and can be used as a baseline study in the domain of aspect-based sentiment analysis.

scholarly journals Comparative Analysis of Supervised Machine Learning Algorithms to Build a Predictive Model for Evaluating Students’ Performance

2021 ◽  
Vol 17 (02) ◽  
pp. 90
Author(s):  
Inssaf El Guabassi ◽  
Zakaria Bousalem ◽  
Rim Marah ◽  
Aimad Qazdar
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Predictive Model ◽  
Learning Algorithms ◽  
Research Work ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
The Future ◽  
Log Linear

In recent years, the world's population is increasingly demanding to predict the future with certainty, predicting the right information in any area is becoming a necessity. One of the ways to predict the future with certainty is to determine the possible future. In this sense, machine learning is a way to analyze huge datasets to make strong predictions or decisions. The main objective of this research work is to build a predictive model for evaluating students’ performance. Hence, the contributions are threefold. The first is to apply several supervised machine learning algorithms (i.e. ANCOVA, Logistic Regression, Support Vector Regression, Log-linear Regression, Decision Tree Regression, Random Forest Regression, and Partial Least Squares Regression) on our education dataset. The second purpose is to compare and evaluate algorithms used to create a predictive model based on various evaluation metrics. The last purpose is to determine the most important factors that influence the success or failure of the students. The experimental results showed that the Log-linear Regression provides a better prediction as well as the behavioral factors that influence students’ performance.

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

2017 ◽  
Vol 4 (1) ◽  
pp. 56-74 ◽  
Author(s):  
Abinash Tripathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Performance Parameters ◽  
Linear Discriminant ◽  
Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

2020 ◽  
pp. 143-163
Author(s):  
Abinash Tripathy ◽  
Santanu Kumar Rath
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Evaluation Report ◽  
Linear Discriminant ◽  
Learning Techniques

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

Urdu Sentiment Analysis Using Supervised Machine Learning Approach

2017 ◽  
Vol 32 (02) ◽  
pp. 1851001 ◽  
Author(s):  
Neelam Mukhtar ◽  
Mohammad Abid Khan
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Nearest Neighbor ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Ensemble Of Classifiers ◽  
Machine Learning Approach ◽  
Better Than

From the last decade, Sentiment Analysis of languages such as English and Chinese are particularly the focus of attention but resource poor languages such as Urdu are mostly ignored by the research community, which is focused in this research. After acquiring data from various blogs of about 14 different genres, the data is being annotated with the help of human annotators. Three well-known classifiers, that is, Support Vector Machine, Decision tree and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) are tested, their outputs are compared and their results are ultimately improved in several iterations after taking a number of steps that include stop words removal, feature extraction, identification and extraction of important features. extraction. Initially, the performance of the classifiers is not satisfactory as the accuracy achieved by all the three is below 50%. Ensemble of classifiers is also tried but the results are not fruitful (in terms of high accuracy). The results are analyzed carefully and improvements are made including feature extraction that raised the performance of these classifiers to a satisfactory level. It is further concluded that [Formula: see text]-NN is performing better than Support Vector Machine and Decision tree in terms of accuracy, precision, recall and [Formula: see text]-measure.

scholarly journals Tutorial: Applying Machine Learning in Behavioral Research

2020 ◽  
Author(s):  
Stephanie Turgeon ◽  
Marc Lanovaz
Keyword(s):  
Machine Learning ◽  
Behavior Analysis ◽  
Gradient Descent ◽  
Learning Algorithms ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Research Questions

Machine learning algorithms hold promise in revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine learning algorithms with novel research questions and datasets.

scholarly journals Supervised Machine Learning Algorithms for Sentiment Analysis of Bangla Newspaper

2021 ◽  
Vol 11 (2) ◽  
pp. 15-23
Author(s):  
Sabrina Jahan Maisha ◽  
Nuren Nafisa ◽  
Abdul Kadar Muhammad Masum
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Nearest Neighbor ◽  
Online News ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Aged People

We can state undoubtedly that Bangla language is rich enough to work with and implement various Natural Language Processing (NLP) tasks. Though it needs proper attention, hardly NLP field has been explored with it. In this age of digitalization, large amount of Bangla news contents are generated in online platforms. Some of the contents are inappropriate for the children or aged people. With the motivation to filter out news contents easily, the aim of this work is to perform document level sentiment analysis (SA) on Bangla online news. In this respect, the dataset is created by collecting news from online Bangla newspaper archive.  Further, the documents are manually annotated into positive and negative classes. Composite process technique of “Pipeline” class including Count Vectorizer, transformer (TF-IDF) and machine learning (ML) classifiers are employed to extract features and to train the dataset. Six supervised ML classifiers (i.e. Multinomial Naive Bayes (MNB), K-Nearest Neighbor (K-NN), Random Forest (RF), (C4.5) Decision Tree (DT), Logistic Regression (LR) and Linear Support Vector Machine (LSVM)) are used to analyze the best classifier for the proposed model. There has been very few works on SA of Bangla news. So, this work is a small attempt to contribute in this field. This model showed remarkable efficiency through better results in both the validation process of percentage split method and 10-fold cross validation. Among all six classifiers, RF has outperformed others by 99% accuracy. Even though LSVM has shown lowest accuracy of 80%, it is also considered as good output. However, this work has also exhibited surpassing outcome for recent and critical Bangla news indicating proper feature extraction to build up the model.

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