Wyniki wyszukiwania - BazTech

1

Social media analysis of the public perception of urban vehicle access regulations

Ogunkunbi Gabriel, Meszaros Ferenc

Transport Problems

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2023

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T. 18, z. 1

157--168

EN

Heavy motorisation in the wake of increasing urbanisation is one of the significant transport problems cities face today. There are practical measures under the panoply of urban vehicle access regulations (UVARs) used to stimulate sustainable mobility behaviour changes in the urban population and reduce reliance on passenger car travel. However, the adoption and implementation of such measures are often riddled with challenges, particularly building public acceptability and preserving social justice. Overcoming these challenges will also require cities to understand how the mobility needs of residents change over time. Considering the limitations of conventional data-collection and monitoring approaches, this study explored and analysed the public perception of UVARs over 12 years through natural language processing techniques using social media as a data source. The results show that UVARs are a prominent topic in public discussion and that the average sentiment expressed in tweets tended to be more positive than negative, with a gradual increase observed over the 12-year study period. In addition, the patterns observed in the data and the topics modelled were consistent with the events and talking points in society related to UVARs. Hence, this study demonstrates that social media data can help policymakers assess public sentiments during the ideation, design, implementation, and operational phases of UVARs and other transport policy measures.

2

Insights into attitudes towards financial innovations by its users

Garškaitė-Milvydienė Kristina, Maknickienė Nijolė, Tvaronavičienė Manuela

Polish Journal of Management Studies

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2023

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Vol. 27, No. 2

106--119

EN

Significant technological advances have determined the importance of FinTech firms worldwide; they attract substantial investment and put competitive pressure on banks providing traditional services. The development of financial innovations challenges users accustomed to classical financial solutions since trust in financial technologies requires risk assessment, which becomes increasingly complicated. The main participants shaping the attitude towards FinTech are investors, customers, regulators, technology developers, and risk managers. The paper aims to explore FinTech opportunities and challenges, as the public understands them. The authors used scientific sources and employed big data processing methods to evaluate social media users' attitudes towards the FinTech sector. The obtained results revealed that, despite of overall positive attitude, FinTech companies have to pay special attention to investment management and ensuring the security and privacy of clients’ data.

PL

Znaczące postępy technologiczne zdeterminowały znaczenie firm FinTech na całym świecie; przyciągają znaczne inwestycje i wywierają presję konkurencyjną na banki świadczące tradycyjne usługi. Rozwój innowacji finansowych stanowi wyzwanie dla użytkowników przyzwyczajonych do klasycznych rozwiązań finansowych, ponieważ zaufanie do technologii finansowych wymaga oceny ryzyka, która staje się coraz bardziej skomplikowana. Głównymi uczestnikami kształtującymi podejście do FinTech są inwestorzy, klienci, organy regulacyjne, twórcy technologii i menedżerowie ryzyka. Artykuł ma na celu zbadanie możliwości i wyzwań FinTech, tak jak rozumie je opinia publiczna. Autorzy wykorzystali źródła naukowe i zastosowali metody przetwarzania dużych zbiorów danych, aby ocenić nastawienie użytkowników mediów społecznościowych do sektora FinTech. Uzyskane wyniki pokazały, że pomimo ogólnie pozytywnego nastawienia, firmy FinTech muszą zwracać szczególną uwagę na zarządzanie inwestycjami oraz zapewnienie bezpieczeństwa i prywatności danych klientów.

3

Sentiment Analysis Framework using Deep Active Learning for Smartphone Aspect Based Rating Prediction

Muralidhar Rathan, Hulipalled Vishwanath R.

Foundations of Computing and Decision Sciences

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2023

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Vol. 48, No. 2

181--209

EN

Social media are a rich source of user generated content where people express their views towards the products and services they encounter. However, sentiment analysis using machine learning models are not easy to implement in a time and cost effective manner due to the requirement of expert human annotators to label the training data. The proposed approach uses a novel method to remove the neutral statements using a combination of lexicon based approach and human effort. This is followed by using a deep active learning model to perform sentiment analysis to reduce annotation efforts. It is compared with the baseline approach representing the neutral tweets also as a part of the data. Considering brands require aspect based ratings towards their products or services, the proposed approach also categorizes predicting ratings of each aspect of mobile device.

4

Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccine

Bansal Anmol, Choudhry Arjun, Sharma Anubhav, Susan Seba

Computer Science

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2023

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T. 24 (2)

163--182

EN

Covid-19 has spread across the world and many different vaccines have been developed to counter its surge. To identify the correct sentiments associated with the vaccines from social media posts, we fine-tune various state-of-the-art pretrained transformer models on tweets associated with Covid-19 vaccines. Specifically, we use the recently introduced state-of-the-art RoBERTa, XLNet, and BERT pre-trained transformer models, and the domain-specific CT-BER and BERTweet transformer models that have been pre-trained on Covid-19 tweets. We further explore the option of text augmentation by oversampling using the language model-based oversampling technique (LMOTE) to improve the accuracies of these models - specifically, for small sample data sets where there is an imbalanced class distribution among the positive, negative and neutral sentiment classes. Our results summarize our findings on the suitability of text oversampling for imbalanced, small-sample data sets that are used to fine-tune state-of-the-art pre-trained transformer models as well as the utility of domain-specific transformer models for the classification task.

5

Disinformation and Polarization in the Online Debate During the 2020 Presidential Election in Poland

Domalewska Dorota

Safety & Defense

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2021

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1

14--24

EN

The deliberate manipulation of public opinion, the spread of disinformation, and polarization are key social media threats that jeopardize national security. The purpose of this study is to analyze the impact of the content published by social bots and the polarization of the public debate on social media (Twitter, Facebook) during the presidential election campaign in Poland in 2020. This investigation takes the form of a quantitative study for which data was collected from the public domains of Facebook and Twitter (the corpus consisted of over three million posts, tweets and comments). The analysis was carried out using a decision algorithm developed in C# that operated on the basis of criteria that identified social bots. The level of polarization was investigated through sentiment analysis. During the analysis, we could not identify automated accounts that would generate traffic. This is a result of an integrated action addressing disinformation and the proliferation of bots that mobilized governments, cybersecurity and strategic communication communities, and media companies. The level of disinformation distributed via social media dropped and an increasing number of automated accounts were removed. Finally, the study shows that public discourse is not characterized by polarization and antagonistic political preferences. Neutral posts, tweets and comments dominate over extreme positive or negative opinions. Moreover, positive posts and tweets are more popular across social networking sites than neutral or negative ones. Finally, the implications of the study for information security are discussed.

6

Does the sentiment of social media post affects positiveengagement of a fan base? : a study on lexiconand convolutional neural network sentiment classifierand ’large’ Twitter accounts

Hachaj Tomasz

Schedae Informaticae

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2020

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Vol. 29

23--37

EN

There are many open questions in this area of computer science that are very important from the perspective of the social media marketing. Among them is: "how to write the messages that are 'popular and liked'"? In this paper we will model and investigate one possible aspect of this issue: does sentiment of the social media post correlates with social engagement of fan base? We have modeled sentiment scoring of social media post using lexicon - based method and by state of the art convolutional neural network. The evaluation of those models has been performed using social media Twitter accounts of five worldknown politicians and celebrities, four brands, two bloggers and two users. We have investigated the various statistical dependencies between sentiment - based scores and engagement scores values. Basing on results we can concluded that number of favorites or shares (both are among the most popular engagement scoring methods that are present in most social media platforms) is not dependent on the sentiment of the message. It does not matter if posts have positive or negative sentiment. The results we have obtained are very important especially for researchers and business entities who utilizes social media platform. Large number of social media scoring algorithms utilizes some kind of binary sentiment analysis associated with social engagement scoring. Our results are strong indicators that two popular sentiment analysis methods should not be used as the predictors of mentioned social engagement scores. Our research can be easily reproduced because we publish both our data and source code of programs we used for evaluation.

7

Towards a new deep learning algorithm based on GRU and CNN : NGRU

Atassi Abdelhamid, el Azami Ikram

Journal of Automation Mobile Robotics and Intelligent Systems

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2020

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Vol. 14, No. 4

45--47

EN

This paper describes our new deep learning system based on a comparison between GRU and CNN. Initially we start with the first system which uses Convolutional Neural Network (CNN) which we will compare with the second system which uses Gated Recurrent Unit (GRU). And through this comparison we propose a new system based on the positive points of the two previous systems. Therefore, this new system will take the right choice of hyper-parameters recommended by the authors of both systems. At the final stage we propose a method to apply this new system to the dataset of different languages (used especially in socials networks).

8

Deep learning hyper-parameter tuning for sentiment analysis in twitter based on evolutionary algorithms

Rodríguez-Barroso Nuria, Moya Antonio R., Fernández José A., Romero Elena, Martínez-Cámara Eugenio, Herrera Francisco

Annals of Computer Science and Information Systems

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2019

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Vol. 18

255–-264

EN

The state of the art in Sentiment Analysis is defined by deep learning methods, and currently the research efforts are focused on improving the encoding of underlying contextual information in a sequence of text. However, those neural networks with a higher representation capacity are increasingly more complex, which means that they have more hyper-parameters that have to be defined by hand. We argue that the setting of hyper-parameters may be defined as an optimisation task, we thus claim that evolutionary algorithms may be used to the optimisation of the hyper-parameters of a deep learning method. We propose the use of the evolutionary algorithm SHADE for the optimisation of the configuration of a deep learning model for the task of sentiment analysis in Twitter. We evaluate our proposal in a corpus of Spanish tweets, and the results show that the hyper-parameters found by the evolutionary algorithm enhance the performance of the deep learning method.

9

Social media in managing political advertising : a study of India

Safiullah M., Pathak P., Singh S., Anshul A.

Polish Journal of Management Studies

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2016

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Vol. 13, No. 2

121--130

EN

Social Media has become an important tool of opinion formation in this technology driven age and marketing managers have realised its significance. With political arena behaving like a customer driven market, uses of marketing technologies are increasingly being used for competitive advantage. Social Media has proved to a useful tool. Marketing political parties are evident in the recent election in India. The present paper explores the implication of twitter on political marketing by studying the relationship between tweet followers and vote share gained by political parties taking Delhi Assembly elections 2015 as a case in point. The findings suggest that there is a positive correlation between the volume of tweet and vote share.

PL

Media społecznościowe w tej erze napędzanej technologią stały się ważnym narzędziem kształtowania opinii i menedżerowie marketingowi zdali sobie sprawę z jej znaczenia. Z areną polityczną zachowującą się jak rynek napędzany klientem, użytkownicy technologii marketingowych są coraz częściej wykorzystywani do przewagi konkurencyjnej. Media społecznościowe okazały się do tego użytecznym narzędziem. Marketingowe partie polityczne są widoczne w ostatnich wyborach w Indiach. Niniejszy artykuł bada wpływ Twittera na marketing polityczny, badając relacje między osobami obserwującymi twittera i udziałem głosów uzyskanych przez partie polityczne, biorące udział w wyborach Zgromadzenia Delhi w 2015. Wnioski sugerują, że istnieje dodatnia korelacja pomiędzy ilością wysyłanych tweetów a udziałem głosów.

10

Automated credibility assessment on twitter

Lorek K., Suehiro-Wiciński J., Jankowski-Lorek M., Gupta A.

Computer Science

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2015

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Vol. 16 (2)

157--168

EN

In this paper, we make a practical approach to automated credibility assessment on Twitter. We describe the process behind the design of an automated classifier for information credibility assessment. As an addition, we propose practical implementation of TwitterBOT, a tool which is able to score submitted tweets while working in the native Twitter interface.

11

Analysing and processing of geotagged social media

Oleksiak P.

Information Systems in Management

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2014

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Vol. 3, No. 4

250--260

EN

The use of location based data analysing tools is an important part of geomarketing strategies among entrepreneurs. One of the key elements of interest is social media data shared by the users. This data is analysed both for its content and its location information, the results help to identify trends represented in the researched regions. In order to verify the possibilities of analysing and processing of geotagged social media data, application programming interfaces (APIs) of social networks were examined for their ability to generate reports from the collected data. The first results of using the system have indicated the possibility of collecting and analysing information generated by Twitter users in real time. Trends and geographical distribution in time can be observed. Further research showed that comparing results and further processing was possible.

12

Aplikacja mikroblogowa-handshake

Kowalczyk P., Słodyczka T., Smyk E., Szadura P.

Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska

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2013

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nr 1

43--45

PL

Artykuł zawiera prezentację możliwości aplikacji mikroblogowej zbudowanej przy pomocy frameworka Pylons w architekturze klient-serwer. Program jest skierowany na urządzenia mobilne wszystkich platform wspierających najnowszą specyfikację HTML5 oraz CSS3. Serwis wzorowany jest na portalu Twitter.

EN

The article contains a presentation of capabilities of microblogging application built with Pylons framework in client/server architecture. The software is designed to work on mobile devices of all platforms supporting HTML5 and CSS3 specification. It is inspired by a popular social networking site - Twitter.