Feasibility of Adding Twitter Data to Aid Drought Depiction: Case Study in Colorado

Abstract The use of social media, such as Twitter, has changed the information landscape for citizens participation in crisis response and recovery activities. Given that drought progression is slow and also spatially extensive, an interesting set of questions arise: How the usage of Twitter by a large population may change during the development of a major drought alongside how changing usage promotes drought detection? For this reason, by investigating contemporary procedures, this paper scrutinizes the potential to advance drought depiction. Hence, an analysis of how social media data, in conjunction with meteorological records, was conducted towards improvement in the detection of drought and furthermore, its progression. The research utilized machine learning techniques applied over satellite-derived drought conditions in Colorado. Specifically, 3 different machine learning techniques were examined: the generalized linear model, support vector machines and deep learning, each applied to test the integration of Twitter data with meteorological records as a predictor of drought development. It is maintained that the data integration of resources is viable given that the Twitter-based model outperformed the control run which did not include social media input. Furthermore the Twitter-based model was superior in predicting drought severity.

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A sentiment analysis system for social media using machine learning techniques: Social enablement

Digital Scholarship in the Humanities ◽

10.1093/llc/fqy037 ◽

2018 ◽

Vol 34 (3) ◽

pp. 569-581 ◽

Cited By ~ 1

Author(s):

Sujata Rani ◽

Parteek Kumar

Keyword(s):

Machine Learning ◽

Social Media ◽

Sentiment Analysis ◽

Media Analysis ◽

Training Data ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Tool ◽

Data Set ◽

Learning Techniques

Abstract In this article, an innovative approach to perform the sentiment analysis (SA) has been presented. The proposed system handles the issues of Romanized or abbreviated text and spelling variations in the text to perform the sentiment analysis. The training data set of 3,000 movie reviews and tweets has been manually labeled by native speakers of Hindi in three classes, i.e. positive, negative, and neutral. The system uses WEKA (Waikato Environment for Knowledge Analysis) tool to convert these string data into numerical matrices and applies three machine learning techniques, i.e. Naive Bayes (NB), J48, and support vector machine (SVM). The proposed system has been tested on 100 movie reviews and tweets, and it has been observed that SVM has performed best in comparison to other classifiers, and it has an accuracy of 68% for movie reviews and 82% in case of tweets. The results of the proposed system are very promising and can be used in emerging applications like SA of product reviews and social media analysis. Additionally, the proposed system can be used in other cultural/social benefits like predicting/fighting human riots.

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Suicide Prediction on Social Media by Implementing Sentiment Analysis along with Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3424.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 4833-4837

Keyword(s):

Machine Learning ◽

Social Media ◽

Semantic Analysis ◽

Machine Learning Techniques ◽

Suicide Prediction ◽

The People ◽

Learning Techniques ◽

Use Of Social Media ◽

Day By Day

Technology is growing day by day and the influence of them on our day-to-day life is reaching new heights in the digitized world. Most of the people are prone to the use of social media and even minute details are getting posted every second. Some even go to the extent of posting even suicide related issues. This paper addresses the issue of suicide and is predicting the suicide issues on social media and their semantic analysis. With the help of Machine Learning techniques and semantic analysis of sentiments the prediction and classification of suicide is done. The model of approach is a four-tier approach, which is very beneficial as it uses the twitter4J data by using weka tool and implementing it on WordNet. The precision and accuracy aspects are verified as the parameters for the performance efficiency of the procedure. We also give a solution for the lack of resources regarding the terminological resources by providing a phase for the generation of records of vocabulary also.

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Big five personality prediction based in Indonesian tweets using machine learning methods

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1973-1981 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1973

Author(s):

Warih Maharani ◽

Veronikha Effendy

Keyword(s):

Machine Learning ◽

Social Media ◽

Big Five ◽

Machine Learning Techniques ◽

Social Characteristics ◽

Support Vector ◽

Big Five Personality ◽

Semantic Features ◽

Learning Techniques ◽

Personality Prediction

<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>

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Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

Complexity ◽

10.1155/2021/5520366 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Samina Amin ◽

Muhammad Irfan Uddin ◽

Duaa H. alSaeed ◽

Atif Khan ◽

Muhammad Adnan

Keyword(s):

Machine Learning ◽

Social Media ◽

Research Work ◽

Media Analysis ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Twitter Data

Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work presents that Social Media Analysis (SMA) can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). For experimental analysis, two datasets (dengue and flu) are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic (ROC) curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques.

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Using Machine Learning Algorithms on Prediction of Stock Price

Journal of Modeling and Optimization ◽

10.32732/jmo.2020.12.2.84 ◽

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.

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A Comparative Study of Different Machine Learning Algorithms for Disease Prediction

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse/v7i7/0177 ◽

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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Predictive modeling for peri-implantitis by using machine learning techniques

Scientific Reports ◽

10.1038/s41598-021-90642-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoaki Mameno ◽

Masahiro Wada ◽

Kazunori Nozaki ◽

Toshihito Takahashi ◽

Yoshitaka Tsujioka ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

Risk Indicators ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Learning Methods ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

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