Social media data analysis to predict mental state of users using machine learning techniques

Over the last period, social media achieved a widespread use worldwide where the statistics indicate that more than three billion people are on social media, leading to large quantities of data online. To analyze these large quantities of data, a special classification method known as sentiment analysis, is used. This paper presents a new sentiment analysis system based on machine learning techniques, which aims to create a process to extract the polarity from social media texts. By using machine learning techniques, sentiment analysis achieved a great success around the world. This paper investigates this topic and proposes a sentiment analysis system built on Bayesian Rough Decision Tree (BRDT) algorithm. The experimental results show the success of this system where the accuracy of the system is more than 95% on social media data.

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SOCIAL MEDIA DATA PROCESSING AND ANALYSIS BY MEANS OF MACHINE LEARNING FOR RAPID DETECTION, ASSESSMENT AND MAPPING THE IMPACT OF DISASTERS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-1237-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 1237-1241

Author(s):

P. M. Kikin ◽

A. A. Kolesnikov ◽

E. A. Panidi

Keyword(s):

Machine Learning ◽

Social Media ◽

Relevant Information ◽

Important Task ◽

Machine Learning Techniques ◽

Social Media Data ◽

Accurate Localization ◽

The Impact ◽

Official Sources ◽

Media Data

Abstract. The main factor determining the possibility of using data obtained from social media as a source of information about the threat of emergencies is their relevance and accuracy. Thus, the important task is the determination of metrics for evaluating these parameters for a specific publication in a social media. It is worth noting the importance of this information channel as a source of eyewitness accounts from the scene. A comparison of social media data and official sources shows that social media contain a significant amount of unique information at different stages of emergency development. Also, when monitoring the situation for a specific event, social media allows to get more relevant information in comparison to official sources. Another important task is to search for emergency messages and their most accurate localization in space. A promising solution for the analysis and processing of social media data during emergency response is the application of artificial intelligence methods, and, particularly, machine learning techniques.

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An approach to analyse and Forecast Social media data using Machine Learning and Data Analysis

2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) ◽

10.1109/iccsea49143.2020.9132895 ◽

2020 ◽

Author(s):

Debabrata Dansana ◽

Janmejoy Das Adhikari ◽

Manas Mohapatra ◽

Subhashree Sahoo

Keyword(s):

Machine Learning ◽

Social Media ◽

Data Analysis ◽

Social Media Data ◽

Media Data

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Hybrid features prediction model of movie quality using Multi-machine learning techniques for effective business resource planning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201844 ◽

2021 ◽

Vol 40 (5) ◽

pp. 9361-9382 ◽

Cited By ~ 1

Author(s):

Naeem Iqbal ◽

Rashid Ahmad ◽

Faisal Jamil ◽

Do-Hyeun Kim

Keyword(s):

Machine Learning ◽

Social Media ◽

Resource Planning ◽

Experimental Results ◽

Quality Prediction ◽

Classification Models ◽

Hybrid Features ◽

Social Media Data ◽

Media Data

Quality prediction plays an essential role in the business outcome of the product. Due to the business interest of the concept, it has extensively been studied in the last few years. Advancement in machine learning (ML) techniques and with the advent of robust and sophisticated ML algorithms, it is required to analyze the factors influencing the success of the movies. This paper presents a hybrid features prediction model based on pre-released and social media data features using multiple ML techniques to predict the quality of the pre-released movies for effective business resource planning. This study aims to integrate pre-released and social media data features to form a hybrid features-based movie quality prediction (MQP) model. The proposed model comprises of two different experimental models; (i) predict movies quality using the original set of features and (ii) develop a subset of features based on principle component analysis technique to predict movies success class. This work employ and implement different ML-based classification models, such as Decision Tree (DT), Support Vector Machines with the linear and quadratic kernel (L-SVM and Q-SVM), Logistic Regression (LR), Bagged Tree (BT) and Boosted Tree (BOT), to predict the quality of the movies. Different performance measures are utilized to evaluate the performance of the proposed ML-based classification models, such as Accuracy (AC), Precision (PR), Recall (RE), and F-Measure (FM). The experimental results reveal that BT and BOT classifiers performed accurately and produced high accuracy compared to other classifiers, such as DT, LR, LSVM, and Q-SVM. The BT and BOT classifiers achieved an accuracy of 90.1% and 89.7%, which shows an efficiency of the proposed MQP model compared to other state-of-art- techniques. The proposed work is also compared with existing prediction models, and experimental results indicate that the proposed MQP model performed slightly better compared to other models. The experimental results will help the movies industry to formulate business resources effectively, such as investment, number of screens, and release date planning, etc.

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