scholarly journals A comparative study of various text mining approaches for analysis of drug reaction using social media post

2019 ◽  
Vol 7 (4) ◽  
pp. 646-651
Author(s):  
Smruti J. Dave ◽  
Prof. Hardik H. Maheta
Keyword(s):  
Social Media ◽  
Drug Reaction ◽  
Text Mining ◽  
Comparative Study

scholarly journals Disaster Reporting and Alert System Using Tweets in a Social Media

2019 ◽  
pp. 176-181
Author(s):  
P. Tamije Selvy ◽  
V. Suriya Prakash ◽  
S. Shriram ◽  
N. Vimalesh
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Comparative Study ◽  
Alert System ◽  
Accuracy Rate ◽  
The Social ◽  
Short Period ◽  
Disaster Reporting ◽  
Centralized Database

The number of Social Media users have increased rapidly these days and a lot of valuable as well as non valuable information is shared in the social which is capable of reaching many people in a short period of time and hence the valuable information that are shared in the social media can be used for many types of analysis. In this paper the tweets that are shared in the name of a disaster is taken and then a alert system is build. This alert system gives alert to the users after checking the received data with the centralized database. This paper also gives a comparative study on the algorithm used in extracting the data from the social media which gives us the accuracy rate of different algorithm that can be used for text mining.

Subjective Text Mining for Arabic Social Media

2017 ◽  
Vol 13 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Nourah F. Bin Hathlian ◽  
Alaaeldin M. Hafez
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Research Area ◽  
Arabic Text ◽  
Classification Methods ◽  
Learning Mechanisms ◽  
Subjective Analysis ◽  
Related Information ◽  
Data Source ◽  
Rich Content

The need for designing Arabic text mining systems for the use on social media posts is increasingly becoming a significant and attractive research area. It serves and enhances the knowledge needed in various domains. The main focus of this paper is to propose a novel framework combining sentiment analysis with subjective analysis on Arabic social media posts to determine whether people are interested or not interested in a defined subject. For those purposes, text classification methods—including preprocessing and machine learning mechanisms—are applied. Essentially, the performance of the framework is tested using Twitter as a data source, where possible volunteers on a certain subject are identified based on their posted tweets along with their subject-related information. Twitter is considered because of its popularity and its rich content from online microblogging services. The results obtained are very promising with an accuracy of 89%, thereby encouraging further research.

scholarly journals Family’s Role in Achieving Language Affiliation among Children: A Comparative Study Applied to Some Families

2021 ◽  
Vol 12 (3) ◽  
pp. 111-128
Author(s):  
Aljohara Fahad Al Saud
Keyword(s):  
Social Media ◽  
Saudi Arabia ◽  
Comparative Study ◽  
Analytical Approach ◽  
Kingdom Of Saudi Arabia ◽  
Language Identity ◽  
Study Population ◽  
High Degree ◽  
Very High

Identifying language affiliation among children for family immigrants is crucial for one’s language identity. This study aimed to determine the role played by Arab families in the Kingdom of Saudi Arabia, Austria, and Britain to attain language affiliation among their children. It also aims to identify the challenges facing families living in these countries in achieving language affiliation among their children. The study population consisted of all the families that live in the Kingdom of Saudi Arabia, in addition to all the Arab families that live in Austria and Britain and the study sample included (120) parents. The researcher adopted the descriptive-analytical approach and used the questionnaire as the study tool. The study reached several results; first, the role played by families in the Kingdom of Saudi Arabia, Austria, and United Kingdom to attain language affiliation among their children got a high degree of response. Second, the challenges facing activating the family’s role in attaining language affiliation of their children in the Kingdom of Saudi Arabia and Austria have got a high degree of response, while in Britain, they obtained a very high degree of response. The study recommended involving all family members in accessing different and creative ways of practicing their native language and activating the role of social media in developing the language affiliation of children.

scholarly journals Predicting real estate market trends and value using pre-processing and sentiment text mining analysis

2021 ◽  
pp. 35-43
Author(s):  
Nikolay Sinyak ◽  
Singh Tajinder ◽  
Jaglan Madhu Kumari ◽  
Vitaliy Kozlovskiy
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Real Estate ◽  
Real Estate Market ◽  
Media Analysis ◽  
Market Information ◽  
Real Estate Markets ◽  
The Real Estate ◽  
Crowd Wisdom ◽  
Market Trends

Ubiquitous growth in the text mining field is unprecedented, where social media mining is playing a significant role. Gigantic growth of text mining is becoming a potential source of crowd wisdom extraction and analysis especially in terms of text pre-processing and sentiment analysis. The analysis of a potential influence of sentiment on real estate markets controversially discussed by scholars of finance, valuation and market efficiency supporters. Therefore, it’s a significant task of current research purview which not only provide an appropriate platform for the contributors but also for active real estate market information seekers. Text mining has gained the widespread attention of real estate market information users which is almost on explosion level. Accessibility of data on such behemoth scale mandates regular and critical analysis of this information for various perspectives’ plausibility. Rich patterns of online social text can be exploited to extract the relevant real estate information effectively. As text mining plays a significant and crucial role in discovery of these insights therefore its challenges and contribution in social media analysis must be explored extensively. In this paper, we provide a brief about the current summary of the modern state of text mining in pre-processing and sentiment for the real estate market analysis. Empha-sis is placed on the resources and learning mechanism available to real estate researchers and practitioners, as well as the major text mining tasks of interest to the community. Thus, the main aim of this chapter is to expound and intellectualize the domains of social media which are accessible on an extraordinary range in the field of text mining real estate for predicting real estate market trends and value.

scholarly journals PENERAPAN TEXT MINING UNTUK MELAKUKAN CLUSTERING DATA TWEET AKUN BLIBLI PADA MEDIA SOSIAL TWITTER MENGGUNAKAN K-MEANS CLUSTERING

2022 ◽  
Vol 10 (4) ◽  
pp. 583-593
Author(s):  
Syiva Multi Fani ◽  
Rukun Santoso ◽  
Suparti Suparti
Keyword(s):  
Social Media ◽  
Text Mining ◽  
Virtual Networks ◽  
Number Of Clusters ◽  
Silhouette Coefficient ◽  
Twitter Account ◽  
Computer Based ◽  
Twitter Users ◽  
Clustering Data ◽  
Coefficient Method

Social media is computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities. Twitter is one of the most popular social media in Indonesia which has 78 million users. Businesses rely heavily on Twitter for advertising. Businesses can use these types of tweet content as a means of advertising to Twitter users by Knowing the types of tweet content that are mostly retweeted by their followers . In this study, the application of Text Mining to perform clustering using the K-means clustering method with the best number of clusters obtained from the Silhouette Coefficient method on the @bliblidotcom Twitter tweet data to determine the types of tweet content that are mostly retweeted by @bliblidotcom followers. Tweets with the most retweets and favorites are discount offers and flash sales, so Blibli Indonesia could use this kind of tweet to conduct advertising on social media Twitter because the prize quiz tweets are liked by the @bliblidotcom Twitter account followers.

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