Web mining based on one-dimensional Kohonen’s algorithm: analysis of social media websites

2016 ◽  
Vol 28 (S1) ◽  
pp. 641-645 ◽  
Author(s):  
Dawei Liu ◽  
Zilong Zhang ◽  
Xiaohong Guo
Keyword(s):  
Social Media ◽  
Web Mining ◽  
Algorithm Analysis ◽  
One Dimensional ◽  
Kohonen’S Algorithm

scholarly journals Ultra-Right Radicalization: a Method of Automated Threat Detection with the Use of Web Mining

2021 ◽  
pp. 30-43
Author(s):  
Anna Karpova ◽  
Aleksey Savel'ev ◽  
Aleksandr Vil'nin ◽  
Anastasiya Kayda ◽  
Sergey Kuznecov ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Web Mining ◽  
Social Science Research ◽  
Science Research ◽  
Automated Detection ◽  
Media Content ◽  
Threat Detection ◽  
Media Content Analysis ◽  
Current Trends

The paper provides a brief review of current trends in studying ultra-right radicalization risks both in Russia and globally. Since the scientific interpretations in studying the notion of radicalization are differentiated, the authors prefer the following one: the ultra-rightists represent communities and movements that accept the idea that violence is necessary to achieve any goal (political, ideological, economic, social or personal). The ultra-rightists justify and promote this idea, expressing their willingness to act violently. They also make a moral commitment to defend those who promote the idea. The authors present the results of the work of the TPU’s cross-subject project team to create a prototype and a method for automated detection of ultra-rightists’ threats in social media. The paper describes the main challenges the researches face when applying smart social media content analysis as a tool for automating social science research.

WEB MINING IN CLASSIFYING YOUTH EMOTIONS

2018 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Zura Izlita Razak ◽  
Shuzlina Abdul rahman ◽  
Sofianita Mutalib ◽  
Nurzeatul Hamimah Abdul hamid
Keyword(s):  
Social Media ◽  
Web Mining ◽  
Daily Activities ◽  
The Internet ◽  
Emotional States ◽  
States Of Mind ◽  
Social Settings ◽  
The Social ◽  
Triggering Events ◽  
Mining Model

Social media sites are websites used as mediums to create and share various types of contents over the internet. These sites can also be accessed through applications on mobile gadgets. Different social media sites are available for free, and most teenagers or youths have at least one active account. They use social media sites to connect and share their online profiles, daily activities, stories, and emotions. Depending on their social settings, their activities may or may not be seen by others. One of the latest trends that is spreading over the social media is the Korean Pop entertainment or popularly known as KPop. Over the social media, youths share and express how they feel about their Korean celebrities, music, and drama. However, the issue of excessive sharing of emotion-sharing over social media may increase the risk of mental illness and affect their mental health. Their obsession to keep up-to-date with their idols might lead or cause adverse consequences on their emotional states of mind. Thus, the aim of this research is to study the changes of youths’ emotions in two different countries which are Malaysia and Korea that are related to the KPop trend. We extract texts from tweets from Twitter social media sites using the Twitter API as the basis of our study. Then, the keyword 'KPop' is used to filter the tweets. Web mining model classifies the 12,000 tweets into six emotion categories, which are joy, sadness, fear, anger, disgust, and surprise. The system then records the emotion changes and the triggering events respectively. 

scholarly journals Web Mining System in a Natural Language Processing Based for Social Media Analysis

2019 ◽  
Vol 8 (S3) ◽  
pp. 72-75
Author(s):  
Gadamsetty Vasavi ◽  
T. Sudha
Keyword(s):  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Web Mining ◽  
Free Form ◽  
Mining System ◽  
Processing Methods ◽  
Social Media Monitoring ◽  
Media Monitoring

Social Media Monitoring and Analysis are the new trends in technology business. The challenge is to extract correct information from free-form texts of social media communication. Natural Language Processing methods are sometimes used in social media monitoring to improve accuracy in extracting information. This paper discusses a web mining system that is based on Natural Language Processing to analyze social media information. In that process, this research examines Natural Language methods that are important for such analysis. Then the traditional web mining steps are discussed along with proposed use of Natural Language Processing methods.

scholarly journals Pengembangan Modul PreprocessingTeks untuk Kasus Formalisasi dan Pengecekan Ejaan Bahasa Indonesia pada Aplikasi Web Mining Simple Solution (WMSS)

2018 ◽  
Vol 15 (2) ◽  
pp. 95
Author(s):  
Umi Chuzaimah Zulkifli
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Web Mining ◽  
Edit Distance ◽  
Correction Method ◽  
Spelling Correction ◽  
The Social ◽  
Abstract Data ◽  
Rule Method ◽  
Bahasa Indonesia

Abstract Data of social media currently has been much used to analyze both sentiment analysis and another analysis. In fact, data that is obtained from the social media in generally has some mistakes which can influence the spelling in writing of words. The solution offered is word formalization and spelling check. Based on the problem, it will be built a preprocessing model to overcome two the mistakes. The method that will be used in formalization is to change the words to be formal form based on KBBI, while the method  used  for spelling check is spelling correction. Spelling correction method consists of distance edit, bigram and distance edit rule. In this study, in addition the application of both methods, also it will be analyzed comparing the result of spelling correction. From the result of analysis shows that distance edit rule has higher accuracy, namely 83.39% than using both edit distance and bigram method. In addition, edit distance rule method also has faster performance than another both methods. Overall, method to change word to formal word were based on KBBI and spelling correction has been able to overcome the problem of two cases, such that it can increase accuracy of  the result of the analysis. Keywords: preprocessing, spelling correction, edit distance, bigram AbstrakData media sosial saat ini telah banyak digunakan untuk melakukan analisis baik analisis sentimen maupun analisis terkait lainnya. Nyatanya, data yang diperoleh dari media sosial tersebut pada umumnya memiliki kesalahan yang akan mempengaruhi hasil analisis. Kesalahan tersebut berupa penggunaan kata yang tidak baku dan adanya kesalahan ejaan dalam penulisan kata. Solusi yang ditawarkan berupa formalisasi kata dan pengecekan ejaan. Berdasarkan masalah tersebut, akan dibangun modul preprocessing untuk mengatasi dua kesalahan di atas. Metode yang digunakan pada formalisasi adalah mengubah kata ke bentuk formal berdasarkan KBBI sedangkan metode yang digunakan pada pengecekan ejaan adalah spelling correction. Metode spelling correction tersebut terdiri dari tiga yaitu edit distance, bigram dan edit distance + rule. Pada penelitian ini, selain penerapan kedua metode juga akan dilakukan analisis untuk melihat perbandingan hasil pada metode spelling correction. Dari hasil analisis tersebut, diketahui bahwa metode edit distance + rule memiliki akurasi yang lebih tinggi yaitu sebesar 83,39% dibandingkan dengan kedua metode lainnya yaitu edit distance dan bigram. Selain itu, metode edit distance + rule juga memiliki performa tercepat dibandingkan kedua metode lainnya. Secara keseluruhan, metode mengubah kata ke bentuk formal berdasarkan KBBI dan spelling correction telah mampu mengatasi masalah pada dua kasus di atas sehingga dapat meningkatkan akurasi hasil analisis. Kata Kunci:preprocessing, spelling correction, edit distance, bigram

Personal Financial Aggregation and Social Media Mining

2014 ◽  
Vol 5 (4) ◽  
pp. 14-25 ◽  
Author(s):  
Vipul Gupta ◽  
Sameer Khanna ◽  
Iljoo Kim
Keyword(s):  
Social Media ◽  
Financial Institutions ◽  
Credit Card ◽  
Web Mining ◽  
Service Providers ◽  
Media Analysis ◽  
Insurance Companies ◽  
Social Media Mining ◽  
Managerial Implications ◽  
Media Mining

Consumers have been banking and trading online for several years now. More ambitious and tech savvy consumers have also been constructing an overview of their financial life by using Personal Finance software like Quicken and online tools such as Yodlee and Mint.com. Since late 1999, Personal Financial Aggregators (PFAs) have started offering internet based services to automate this process of account aggregation. This web account aggregation allows individuals to log onto one Web site and view all of their online accounts in one place. Online accounts that can be aggregated include financial sites (bank, credit card, brokerage, insurance, etc.) as well as lifestyle-based sites (travel awards, email, chat rooms, etc.). The idea behind Personal Financial Aggregation is to offer consumers their own personal portal from which they can see all their finances at a glance, balance and rebalance accounts, make investments, pay bills, etc. In addition to this Web data aggregation, consumers are relying on social media sites such as facebook, tweeter and other internet forums to get financial advice from each other and also to critique various financial products and services. As a result, many Financial Institutions (FIs) are using social media analysis and mining to shape their businesses. FIs include consumer banks, brokerages, insurance, wealth management firms, etc. This paper presents a framework for financial institutions that combines social media mining, web mining, online advice engines, and web aggregation. This framework can be utilized by FIs to analyze online buzz about their products/services and combine those insights with web aggregation and online advice to create different revenue streams and to offer personalized bundled products and services. The authors conducted interviews with various executives at the Global Financial institutions and insurance companies to test and validate this framework. A comprehensive review of top service providers and vendors that can enable and drive this framework is also discussed in this paper, followed by managerial implications, benefits and challenges.

Web mining for the mayoral election prediction in Taiwan

2017 ◽  
Vol 69 (6) ◽  
pp. 688-701 ◽  
Author(s):  
Jia-Yen Huang
Keyword(s):  
Social Media ◽  
Web Mining ◽  
Semantic Analysis ◽  
Transport Infrastructure ◽  
Content Type ◽  
Part Of Speech ◽  
Phrase Extraction ◽  
Mayoral Election ◽  
Election Prediction ◽  
Election Results

Purpose The prediction of pre-election polls is an issue of concern for both politicians and voters. The Taiwan nine-in-one election held in 2014 ended with jaw-dropping results; apparently, traditional polls did not work well. As a remedy to this problem, the purpose of this paper is to utilize the comments posted on social media to analyze civilians’ views on the two candidates for mayor of Taichung City, Chih-chiang Hu, and Chia-Lung Lin. Design/methodology/approach After conducting word segmentation and part-of-speech tagging for the collected reviews, this study constructs the opinion phrase extraction rules for identifying the opinion words associated with the attribute words. Next, this study classifies the attribute words into six municipal governance-related topics and calculates the opinion scores for each candidate. Finally, this study uses correspondence analysis to transform opinion information on the candidates into a graphical display to facilitate the interpretation of voters’ views. Findings The results show that the topics of candidates’ backgrounds and transport infrastructure were the two most critical factors for the election prediction. Based on the predication, Lin outscores Hu by 17.74 percent which is close to the real election results. Research limitations/implications This study proposes new rules for the extraction of Chinese opinion words associated with attribute words. Practical implications This study applies Chinese semantic analysis to assist in predicting election results and investigating the topics of concern to voters. Originality/value The proposed opinion phrase extraction rules for Chinese social media, as well as the election forecast process, can provide valuable references for political parties and candidates to plan better nomination and election strategies.

ISIS vs. the U.S. government: A war of online video propaganda

First Monday ◽  
2015 ◽  
Author(s):  
William H. Allendorfer ◽  
Susan C. Herring
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Online Video ◽  
Islamic State ◽  
Islamic Culture ◽  
New Members ◽  
One Dimensional ◽  
Shed Light ◽  
The U.S

The Islamic State of Iraq and Syria (ISIS) relies heavily on propaganda in the form of videos distributed over social media to recruit supporters and new members to its cause, including from the U.S. The U.S. government has countered with anti-ISIS propaganda videos; however, sources claim that the U.S. is losing the propaganda war. We evaluate that claim through a comparative multimodal content analysis of the ISIS video Flames of War and the videos posted in response on the U.S. Department of State’s (USDS) Think Again Turn Away YouTube channel. Our findings shed light on some of the reasons why the USDS anti-propaganda videos are less rhetorically effective than the ISIS videos, including a one-dimensional narrative, a stance that could appear inauthentic, and a lack of sensitivity to Islamic culture. In concluding, we advance recommendations that the USDS could follow to strengthen its online propaganda defense against ISIS, and extend the implications of our findings to other social media fronts where the ISIS vs. USDS propaganda war is being waged.

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