Temporal Sentiment Analysis of the Data from Social Media to Early Detection of Cyberbullicide Ideation of a Victim by Using Graph-Based Approach and Data Mining Tools

2020 ◽  
pp. 107-112 ◽  
Author(s):  
A. Chatterjee ◽  
A. Das
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Early Detection ◽  
Sentiment Analysis ◽  
Mining Tools

scholarly journals Sentiment Analysis and Summarization of Social Media Content using Topic Modeling

2020 ◽  
Vol 9 (3) ◽  
pp. 209-212
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Topic Modeling ◽  
Opinion Mining ◽  
Text Documents ◽  
Topic Identification ◽  
Key Factor ◽  
Polarity Classification ◽  
Using Data

Explosion of Web 2.0 had made different social media platforms like Facebook, Twitter, Blogs, etc a data hub for the task of Data Mining. Sentiment Analysis or Opinion mining is an automated process of understanding an opinion expressed by customers. By using Data mining techniques, sentiment analysis helps in determining the polarity (Positive, Negative & Neutral) of views expressed by the end user. Nowadays there are terabytes of data available related to any topic then it can be advertising, politics and Survey Companies, etc. CSAT (Customer Satisfaction) is the key factor for this survey companies. In this paper, we used topic modeling by incorporating a LDA algorithm for finding the topics related to social media. We have used datasets of 900 records for analysis. By analysis, we found three important topics from Survey/Response dataset, which are Customers, Agents & Product/Services. Results depict the CSAT score according to Positive, Negative and Neutral response. We used topic modeling which is a statistical modeling technique. Topic modeling is a technique for categorization of text documents into different topics. This approach helps in better summarization of data according to the topic identification and depiction of polarity classification of sentiments expressed.

Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

2021 ◽  
Vol 2 (2) ◽  
pp. 3-21
Author(s):  
Yassine Drias ◽  
Habiba Drias
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Early Stage ◽  
Rule Mining ◽  
Virus Propagation ◽  
The Third ◽  
Evolution Analysis

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

scholarly journals Machine Learning-Based Data Mining Method for Sentiment Analysis of the Sewol Ferry Disaster's Effect on Social Stress

2020 ◽  
Vol 11 ◽  
Author(s):  
Min-Joon Lee ◽  
Tae-Ro Lee ◽  
Seo-Joon Lee ◽  
Jin-Soo Jang ◽  
Eung Ju Kim
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Social Stress ◽  
Scientific Evidence ◽  
National Level ◽  
Psychological Impact ◽  
Social Media Platforms ◽  
Negative Sentiment ◽  
Media Data

The Sewol Ferry Disaster which took place in 16th of April, 2014, was a national level disaster in South Korea that caused severe social distress nation-wide. No research at the domestic level thus far has examined the influence of the disaster on social stress through a sentiment analysis of social media data. Data extracted from YouTube, Twitter, and Facebook were used in this study. The population was users who were randomly selected from the aforementioned social media platforms who had posted texts related to the disaster from April 2014 to March 2015. ANOVA was used for statistical comparison between negative, neutral, and positive sentiments under a 95% confidence level. For NLP-based data mining results, bar graph and word cloud analysis as well as analyses of phrases, entities, and queries were implemented. Research results showed a significantly negative sentiment on all social media platforms. This was mainly related to fundamental agents such as ex-president Park and her related political parties and politicians. YouTube, Twitter, and Facebook results showed negative sentiment in phrases (63.5, 69.4, and 58.9%, respectively), entity (81.1, 69.9, and 76.0%, respectively), and query topic (75.0, 85.4, and 75.0%, respectively). All results were statistically significant (p < 0.001). This research provides scientific evidence of the negative psychological impact of the disaster on the Korean population. This study is significant because it is the first research to conduct sentiment analysis of data extracted from the three largest existing social media platforms regarding the issue of the disaster.

scholarly journals Applying of a Company’s Stock Price Prediction Using Data Mining

2019 ◽  
Vol 8 (2) ◽  
pp. 2847-2850
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Stock Prices ◽  
Stock Price ◽  
Mining Machine ◽  
Support Vector ◽  
Product Reviews ◽  
Stock Price Prediction

Stock market analysis is a common economic activity that has been an attractive topic to research and used in different forms of day-to-day life in order to predict the stock prices. Techniques like major analysis, Statistical investigation, Time arrangement analysis and so on are reliably worthy forecast device. In this paper, Data mining, Machine learning (ML) and Sentiment analysis are techniques used for analyzing public emotions in order predict the future stock prices. The goal of a project is to review totally different techniques to predict stock worth movement victimization the sentiment analysis from social media, data processing. Sentiment classifiers are designed for social media text like product reviews, blog posts, and email corpus messages. In the company’s communication network, information mining calculation is utilized as to mine email correspondence records and verifiable stock costs. Implementing various Machine learning and Classification models such as Deep Neural network, Random forests, Support Vector Machine, the company can successfully implemented a company-specific model capable of predicting stock price movement with efficient accuracy

Sentiment Analysis, Social Media, and Public Administration

2015 ◽  
pp. 231-249 ◽  
Author(s):  
Daniel José Silva Oliveira ◽  
Paulo Henrique de Souza Bermejo ◽  
Pamela Aparecida dos Santos
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Public Administration ◽  
Opinion Mining ◽  
High Accuracy ◽  
Popular Opinion ◽  
Government Services ◽  
The Subject ◽  
Technological Study ◽  
Mining Tools

This chapter describes how sentiment analysis, based on texts taken from social media, can be an instrument for measuring popular opinion about government services and can contribute to evaluating and developing public administration. This is an applied, interdisciplinary, qualitative, exploratory, and technological study. Throughout the chapter, the main theoretical and conceptual formulations about the subject are reviewed, and practical demonstrations are made using opinion-mining tools that provide high accuracy in data processing. For demonstration purposes, topics that triggered the popular protests of June 2013 in Brazil were selected, involving million people across the country. A total of 51,857 messages posted on social media about these topics were collected, processed, and analyzed. Through that analysis, it can be observed that even after six months, the factors that motivated the protests continued generating citizen dissatisfaction.

Sentiment Analysis, Social Media, and Public Administration

2015 ◽  
pp. 159-176
Author(s):  
Daniel José Silva Oliveira ◽  
Paulo Henrique de Souza Bermejo ◽  
Pamela Aparecida dos Santos
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Public Administration ◽  
Opinion Mining ◽  
High Accuracy ◽  
Popular Opinion ◽  
Government Services ◽  
The Subject ◽  
Technological Study ◽  
Mining Tools

This chapter describes how sentiment analysis, based on texts taken from social media, can be an instrument for measuring popular opinion about government services and can contribute to evaluating and developing public administration. This is an applied, interdisciplinary, qualitative, exploratory, and technological study. Throughout the chapter, the main theoretical and conceptual formulations about the subject are reviewed, and practical demonstrations are made using opinion-mining tools that provide high accuracy in data processing. For demonstration purposes, topics that triggered the popular protests of June 2013 in Brazil were selected, involving million people across the country. A total of 51,857 messages posted on social media about these topics were collected, processed, and analyzed. Through that analysis, it can be observed that even after six months, the factors that motivated the protests continued generating citizen dissatisfaction.

scholarly journals Comparison of Sentiment Analysis against Digital Payment “T-cash and Go-pay” in Social Media Using Orange Data Mining

2019 ◽  
Vol 1 (2) ◽  
pp. 152-163 ◽  
Author(s):  
Novita Anggraini ◽  
Heri Suroyo
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Rule Based

Saat ini pembicaraan publik di sosial media menjadi salah satu hal menarik untuk diteliti. Dari topik pembicaraan itu menghasilkan komentar yang sebagian besar mengandung opini sentimen. Penelitian ini mencoba menganalisis komentar dengan metode analisis vader, yaitu metode analisis lexicon-based berbasis rule-based sentiment analysis. Vader akan menganalisis text berdasarkan lexicon (a library) yang menghasilkan class sentimen berupa  positif, negatif, dan neutral dengan tambahan skor total atau compound (combined score). Penelitian ini memanfaatkan Prepocess text yang meliputi transformation, tokenization, normalization, dan filtering yang bertujuan agar text bisa dianalisis oleh Orange Data Mining guna mendapat perbandingan analisis sentimen terhadap T-cash dan Go-pay di sosial media. Dari penelitian yang dilakukan mendapat kesimpulan bahwa T-cash memiliki nilai sentimen positif lebih tinggi dari pada Go-pay dan memiliki sentimen negatif yang lebih rendah dari pada Go-pay. Namun persamaanya T-cash dan Go-pay memiliki kesamaan pola grafik dimana sentimen terbesar adalah neutral, diikuti oleh positif, dan terakhir adalah negative.

scholarly journals A Survey on Sentiment Analysis Algorithms and Techniques For Arabic Textual Data

2020 ◽  
pp. 74-87
Author(s):  
admin admin ◽  
◽  
◽  
◽  
Gawaher Soliman Hussein ◽  
...  
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Support Vector Machine ◽  
Sentiment Analysis ◽  
Arab World ◽  
Arabic Language ◽  
Emotional Reaction ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Textual Data

The concept Sentiment means the feeling, behavior, belief, or attitude towards something that almost being embedded. sentiment analysis is the process of analyzing, extracting, studying, and classifying the various reviews, opinions are given by people, and human’s emictions into positive, negative, neutral. It is considered one of the most significant scientific branches that aim to determine the behavior of the speaker, the attitude of the writer according to some topic, or the overall emotional reaction to website, document, event, interaction, products, or services. many users can share every day various opinions on different topics that may be detected or embedded by using micro-blogging which considered a rich resource for sentiment analysis and belief mining such as Facebook, Twitter, forums, and Blogs. recently a huge number of posted comments, tweets, and reviews of different social media websites include rich information in addition to most of the on-line shopping sites provide the opportunity to customers to write reviews about products in order to enhance the sales of those products and to improve both of product quality and customer satisfaction. manual analysis of these large reviews is practically impossible thus it is needed to discover an automated approach to solving such a hard process. In the Middle East and particularly in the Arab world, social media websites continue to be the top-visited websites especially with the current social and political changes in this part of the world. the main objective of that research is to differentiate between various algorithms and techniques of sentiment analysis and classification dependent on the Arabic language as a little number of researchers discusses that point relevant to the Arabic language. Different algorithms and techniques of data mining such as Support Vector Machine (SVM), Naïve Bayes (NB), Bayesian Network (BN), Decision tree (DT), k-nearest neighbor (KNN), Maximum Entropy (ME), and Neural Network (NN) in addition to many other alternative techniques which are used for analyzing and classifying textual data. For the reasons of difficulties in analyzing and mining a large number of linguistic words for their Those techniques are estimated based on the Arabic language due to its richness and diversity. The comparison between data mining techniques showed that the most accurate technique is the support vector machine (SVM) algorithm. every successful sentiment depends on two essential analysis tools are language and culture.

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