Twitter Sentiment Data Analysis of User Behavior on Cryptocurrencies

2021 ◽  
pp. 277-291
Author(s):  
Hasitha Ranasinghe ◽  
Malka N. Halgamuge
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Sentiment Analysis ◽  
User Behavior ◽  
User Preference ◽  
Emotional Tone ◽  
The World ◽  
The Way

Social networks such as Twitter contain billions of data of users, and in every second, a large number of tweets trade through Twitter. Sentiment analysis is the way toward deciding the emotional tone behind a series of words that users utilize to understand the attitudes, thoughts, and emotions that are enunciated in online references on Twitter. This chapter aims to determine the user preference of Bitcoin and Ethereum, which are the two most popular cryptocurrencies in the world by using the Twitter sentiment analysis. It proposes a powerful and fundamental approach to identify emotions on Twitter by considering the tweets of these two distinctive cryptocurrencies. One hundred twenty thousand (120,000) tweets were extracted separately from Twitter for each keyword Bitcoin/BTC and Bitcoin/ETC between the period from 12/09/2018 to 22/09/2018 (10 days).

scholarly journals LINGUÍSTICA E PUBLICIDADE: UM OLHAR ENUNCIATIVO SOBRE UMA CAMPANHA PUBLICITÁRIA DO CITIBANK.

2017 ◽  
Author(s):  
Ellen Cristina Gerner Siqueira
Keyword(s):  
Social Networks ◽  
Financial Institution ◽  
Daily Lives ◽  
Advertising Campaign ◽  
Verbal Language ◽  
People’S Daily ◽  
The World ◽  
People's Daily ◽  
The Way

O discurso publicitário está presente no cotidiano das pessoas por meio de diversos tipos de mídia: anúncios na TV, impressos, outdoors ou nas redes sociais. Entre os recursos utilizados pela publicidade para convencer as pessoas sobre os produtos, serviços ou ideias que se deseja vender nos interessa estudar o uso da linguagem verbal, mais especificamente a maneira com que a publicidade constrói sentido por meio da linguagem. Assim, este artigo pretende analisar alguns enunciados de uma campanha publicitária realizada pela instituição financeira Citibank sob o olhar da teoria enunciativa desenvolvida por Oswald Ducrot. A campanha serve como  exemplo do jogo argumentativo que pode ser criado por meio da linguagem verbal, enredado em si mesmo, onde o locutor não fala sobre o mundo, mas fala para construir o mundo e explicitar a sua verdade por meio de argumentação linguística e não, necessariamente, retórica. Abstract: Advertising speech is present in people's daily lives through various types of media: TV ads, print ads, billboards, or social networks. Among the resources used by advertising to convince people about the products, services or ideas they want to sell we are interested in studying the use of verbal language, more specifically the way in which advertising builds meaning through language. Thus, this article intends to analyze some statements of an advertising campaign carried out by the financial institution Citibank under the view of the enunciative theory developed by Oswald Ducrot. The campaign is a great example of the game of argumentation that can be created through verbal language, entangled in itself, in which the speaker does not speak about the world, but speaks to build the world and to explain its truth through linguistic argumentation and not , necessarily, rhetoric.

scholarly journals Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery

2020 ◽  
Author(s):  
Habiba H. Drias ◽  
Yassine Drias
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Association Rules ◽  
Great Majority ◽  
The Other ◽  
Frequent Patterns ◽  
Twitter Data ◽  
Other Hand ◽  
The World

A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.

scholarly journals Perception of nursing faculty on the care: Heidegger constructions

2016 ◽  
Vol 50 (spe) ◽  
pp. 39-46 ◽  
Author(s):  
Luciara Fabiane Sebold ◽  
Silvana Silveira Kempfer ◽  
Juliana Balbinot Reis Girondi ◽  
Marta Lenise Prado
Keyword(s):  
Data Analysis ◽  
Nursing Care ◽  
Nursing Faculty ◽  
The Other ◽  
Way Of Life ◽  
The World ◽  
Heideggerian Hermeneutic ◽  
Being There ◽  
Mode Of Being ◽  
The Way

Objective To understand the perceptions of nursing teachers about care in the light of Heidegger’s framework. It was used as theoretical and methodological reference Hei- degger’s hermeneutics. Method To capture the meanings we used phenomenological interviews with 11 teachers. e data analysis is based on heideggerian hermeneutic. Results The way to be a nurse determines their way of life to the care that re ects the construction of experiences in the nursing worldliness. The existence of the nurse for nursing care is evidenced in care relations established between being careful and being caregiver, deciding the mode of being-there of the nurse who has before him and on the other the possibilities of care. Conclusions It is being in the world that the being-nurse is manifested in their subjectivity in care for sensitive, is the objectivity of scienti c care, and is in the interrelationship with being careful is that manifests the being of choices and existing decisions in his way of being.

scholarly journals Methodology for sentiment analysis in twitter posts about mobile learning

2021 ◽  
Vol 2 (2) ◽  
pp. 2718-2728
Author(s):  
Hernán Gil-Ramírez ◽  
Rosa María Guilleumas-García
Keyword(s):  
Social Networks ◽  
Communication Network ◽  
Sentiment Analysis ◽  
Mobile Learning ◽  
Research Work ◽  
Complex Interactions ◽  
The Individual ◽  
Negative Feelings ◽  
Analysis Of Social Networks ◽  
The Way

Analysis of social networks has become of great interest to researchers from different areas, including educators, due to Twitter’s growing importance as a space for discussion and dissemination of knowledge and opinions. This reality demands the development of analysis processes that allow to know the topics of interest in the network, the positive or negative feelings in relation to those topics and who the network influencers are. Those objectives guided this research work and in order to achieve them, we developed a methodological proposal for sentiment analysis of tweets. This article describes the process followed, which involved 1) detecting the structure of the communication network, 2) calculating the general metrics, 3) representing the communication network, 4) identifying and analyzing the clusters, 5) calculating their metrics as well as those of the individual nodes and 6) establishing the polarity of the posts published in the network. This paper also describes the methodoly followed to identify trends and topics of interest in the hashtags and web domains included in the tweets. The proposal for analysis presented here is intended to help researchers interested in the field of social networks, to understand the complex interactions that take place in these environments and the way in which information is disseminated, valued and converted into topics of interest thanks to the network users’ actions.

Stress Analytical Modelling Based on People’s Views on Social Networks

2018 ◽  
Vol 7 (3.12) ◽  
pp. 466
Author(s):  
John . ◽  
Vivia Mary ◽  
Prashar . ◽  
Aastha . ◽  
Khamesra . ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Wearable Sensors ◽  
Human Mind ◽  
Self Report ◽  
The World ◽  
Collection Data ◽  
Improved Performance ◽  
Bayes Algorithm

With the growing world, the human mind has grown too much with its own complexities. Gone are the days where people used to express themselves through speech or by verbal contact. Now, the era of social media has brought an interface to the world where they can convey their opinions as well as their inner most thoughts through various social networks. People are more comfortable to express their emotions on these social media rather in the real world. This all has led to the need of Sentiment analysis. It has a major role in detecting stress in humans and how surrounding environment is affecting the population of the world. The project analyses the stress among people through tweets. Self-report questionnaires face to face interviews wearable sensors is the main basis of psychological stress that is caused traditionally. The project covers all possible aspects of interactions on social media. Firstly, by fetching tweets from twitter dynamically based on keyword entered by user and segregating them into positive, negative and neutral categories using Naive Bayes algorithm. Secondly, performing sentiment analysis on a dataset containing movie reviews and thirdly, on a very large dataset containing 5 million tweets using Hadoop and an added algorithm of logistic regression for improved performance and efficiency. The entire project was carried out using a distinct step by step procedure consisting of data collection, data cleaning, training of data, data modelling, algorithm application and visualization. Experiments were conducted on an extensive basis to verify the superior theory algorithms and credibility of the project. 

scholarly journals Potential of the dynamic approach to data analysis

2021 ◽  
Vol 258 ◽  
pp. 07012
Author(s):  
Vera Orlova ◽  
Vyacheslav Goiko ◽  
Yulia Alexandrova ◽  
Evgeny Petrov
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
User Behavior ◽  
Learning Algorithm ◽  
Training Sample ◽  
Dynamic Approach ◽  
Tomsk Region ◽  
User Groups ◽  
Analysis Of Social Networks ◽  
Selection Of

Explores the potential of a dynamic data analysis approach to study user behavior in social networks. Currently, information appears on social networks that allows differentiating user groups by their activity within the technical capabilities of a particular social network. The description of the information field of Tomsk is presented, a brief analysis is given. A dynamic approach to the study of user behavior, the structure of nodes and connections of social networks makes it possible to identify the rate of growth or decrease in the size of the network, the redistribution of connections between groups. There are four main stages in the analysis of social networks: 1) data collection; 2) selection of data for analysis; 3) selection and application of the analysis method; and 4) drawing conclusions. To obtain a complete picture of the information field of the Tomsk region, posts for 2019 were unloaded from all regional communities. All posts were classified based on training sample and specialized machine learning algorithm.

Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery (Preprint)

2020 ◽  
Author(s):  
Habiba Drias ◽  
Yassine Drias
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Association Rules ◽  
Great Majority ◽  
The Other ◽  
Frequent Patterns ◽  
Twitter Data ◽  
Other Hand ◽  
The World

UNSTRUCTURED A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.

scholarly journals Dynamic Data Analysis and Decision Making on Twitter Data

2019 ◽  
Vol 9 (1) ◽  
pp. 4010-4015
Keyword(s):  
Decision Making ◽  
Social Networks ◽  
Data Analysis ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Dynamic Data ◽  
Twitter Data ◽  
Bayes Algorithm ◽  
Program Interface ◽  
Processing Steps

It became a tedious task for the data analysts to make decisions on social networks. The existing approaches are not adequate to perform data pre-processing, analysis and decision making on the data dynamically. Therefore, this research aims to propose an approach to data analysis and decision making. The proposed approach emphasis on extracting tweets form twitter API (Application Program Interface), pre-processing the tweets by following seven pre-processing steps. The processed tweets are trained by NLTK (Natural Language Toolkit) and Text Blob are given to the sentiment analysis. Classification is done using the Naive Bayes algorithm to make a decision on processed tweets. The tweets which are related to “MeToo Movement” are considered primarily for decision making and satisfactory results are obtained. It is been observed that the proposed approach is accurate when compared to other approaches.

From tweets to graffiti: ‘I am Charlie’ as a ‘writing event’

2019 ◽  
Vol 12 (2) ◽  
pp. 171-186
Author(s):  
Maëlle Bazin
Keyword(s):  
Social Networks ◽  
Public Space ◽  
Terrorist Attacks ◽  
Local Authorities ◽  
Urban Public Space ◽  
The Public ◽  
The World ◽  
The Media ◽  
The Way

Any visitor who walked the streets of Paris in the days or weeks following the attacks of January 2015 would definitely have witnessed a particular form of graphic irruption: the dissemination of messages of solidarity and mourning, and the repetition, within this mass of writing, of the formula ‘I am Charlie’. Although the situation was different, the responses to terrorist attacks in January 2015 and the 9/11 aftermath are comparable by the ‘writing event’ (Fraenkel, 2002, 2018) they produced: temporary and atypical dispositifs of writing turned to the public space in order to be read or at least seen by passers-by. This article, structured along chronological lines, traces the evolution of the viral formula over the long term from Twitter to the urban public space. Firstly, the author focuses on the origin and meanings of the statement and formulates several hypotheses that may explain its wide circulation on social networks. Secondly, she analyses the post-attack graffiti based on databases of several private graffiti-cleaning companies in order to highlight the temporary sacralization of illegal writings. The ‘ Je suis Charlie’ phenomenon is interesting in many ways: its staggering, massive diffusion; the apparent unanimity with which it was greeted in the world of politics and the media; and the way it was managed by local authorities.

scholarly journals Sentiment Analysis for Social Media

2019 ◽  
Vol 9 (23) ◽  
pp. 5037 ◽  
Author(s):  
Carlos A. Iglesias ◽  
Antonio Moreno
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Special Issue ◽  
Lexical Resources ◽  
Key Technology ◽  
Learning Techniques ◽  
The Way

Sentiment analysis has become a key technology to gain insight from social networks. The field has reached a level of maturity that paves the way for its exploitation in many different fields such as marketing, health, banking or politics. The latest technological advancements, such as deep learning techniques, have solved some of the traditional challenges in the area caused by the scarcity of lexical resources. In this Special Issue, different approaches that advance this discipline are presented. The contributed articles belong to two broad groups: technological contributions and applications.

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