scholarly journals Evaluating Machine Learning Techniques for Detecting Offensive and Hate Speech in South African Tweets

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 21496-21509 ◽  
Author(s):  
Oluwafemi Oriola ◽  
Eduan Kotze
Keyword(s):  
Machine Learning ◽  
South African ◽  
Hate Speech ◽  
Machine Learning Techniques ◽  
Learning Techniques

scholarly journals DETECTION AND CLASSIFICATION OF HATE SPEECH

2021 ◽  
Vol 5 (11) ◽  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Hate Speech ◽  
Machine Learning Techniques ◽  
The People ◽  
Learning Techniques ◽  
The Social ◽  
Improved Technology

The challenges that are to be faced while handling with hate speech is not a new thing. From thepast few years due to the boosted usage of internet, hateful activities across social media is increasing rapidly. Improved technology has made it possible to create a platform where people can feel free to share their opinions and experiences.it wouldn't be a problem if this is just the case. but we can also see hateful comments running throughout the social media targeting a person or a community. Hate speech is the statement that targets a person or community of people discriminating based on caste, creed, nationality etc. Our project aims at resolving the above problem by using Machine Learning techniques to automatically detect hate speech and classify them into various classes such as extremely positive, positive neutral etc. We have used classifier that works based on the lexicons and finally compare it with other classifiers that doesn't use lexicons. Aimed beneficiaries of this model are the people who are being targeted on social media. Based on the results they can calculate intensity of the comments.

scholarly journals Identification of HATE speech tweets in Pashto language using Machine Learning techniques

2021 ◽  
Vol 10 (3) ◽  
pp. 1501-1508
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Decision Tree ◽  
Sentiment Analysis ◽  
Hate Speech ◽  
Research Work ◽  
Machine Learning Techniques ◽  
Related Data ◽  
Learning Techniques ◽  
The Impact

From the last few years, researchers are very much attracted to sentiment analysis, especially towards hate speech detectionsystems. As in different languages procreation of hate speech has compelling and symbolic consideration on social media. Hate speech has a great impact on society, using hate words harms others dignity. Hate speech detectionsystems areimportant to stop the transformation of hate words into crimes. In this research,a frameworkis developedfor hate speech detectionsystemin the Pashto language. A datasetis created for which data is collected from Twitter. Because there is no related data available. Most of the research work has been done in this domain for other languages, and it’s very maturein the context of detecting hate speech. But when it arrives at the morphological languages not much work has been done especially in the Pashto language. This researchaimed and collected data from Twitter, Tweets related to ethnicity and religion. The data collected from twitter has been annotated manually and categorized the data as hate or not by comparing it with the offensive content. For hate speechdetection systemsto view the impact of different features/attribute this study performed experiments on the existing classifiers i.e.,SVM, Naïve Bayes, Decision tree and KNN. SVM produced the highest result at dataset of 500 i.e.,74% among all the classifiers. KNN and Decision Tree produced same result at dataset of 1500 i.e.,65.0%. Dataset of 2800 Decision Tree produced the highest result i.e.,72% and SVM produced 71.9%.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques

A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

2017 ◽  
Vol 7 (7) ◽  
pp. 172
Author(s):  
Anantvir Singh Romana
Keyword(s):  
Machine Learning ◽  
Subsequent Treatment ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Classification Problems ◽  
Learning Techniques ◽  
Neural Network Classifiers ◽  
Diagnostic Detection

Accurate diagnostic detection of the disease in a patient is critical and may alter the subsequent treatment and increase the chances of survival rate. Machine learning techniques have been instrumental in disease detection and are currently being used in various classification problems due to their accurate prediction performance. Various techniques may provide different desired accuracies and it is therefore imperative to use the most suitable method which provides the best desired results. This research seeks to provide comparative analysis of Support Vector Machine, Naïve bayes, J48 Decision Tree and neural network classifiers breast cancer and diabetes datsets.

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