Wyniki wyszukiwania - BazTech

1

Social media for brand image manipulation: an automotive industry applied approach

Vagianos Dimitrios

Scientific Journal of the Military University of Land Forces

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2024

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Vol. 56, No. 1(211)

97--121

EN

The paper illustrates how social media marketing and analytics can assist businesses in achieving branding objectives by increasing their social media impact through advertising, getting social, using appropriate keywords, and creating effective and interactive communication channels with their intended audience. The multifaceted influence of social media postings is demonstrated by leveraging the Toyota Motor Corporation example. An explanation of how defamatory incidents have impacted the company's social media atmospheric image is also included. Evidence of how negative intervals of social media presence could have been intelligently reversed through efficient content infusions is presented. Among numerous instruments allowing for analysis of marketing strategy results, "Social Mention", "Talkwalker" and "Mentiolytics" were selected in order to demonstrate the efficiency and utility of social media monitoring methods using freely available tools. In this study, Toyota’s social media marketing strategy is highlighted by presenting measurements of variables such as the reach, strength, passion and sentiment of the brand over randomly selected time windows, demonstrating the dynamically evolving field of social media monitoring techniques.

PL

Artykuł przedstawia, w jaki sposób social media marketing i analityka mogą wspomóc branże w osiągnięciu celów kreowania marki przez zwiększenie wpływu ich mediów społecznościowych z pomocą reklam, relacji ze swoją społecznością, używania właściwych słów kluczowych oraz kreowania skutecznych i interaktywnych kanałów komunikacji z potencjalnymi użytkownikami. Wieloaspektowy wpływ postów w mediach społecznościowych został zademonstrowany na przykładzie Toyota Motor Company wraz z negatywnymi przypadkami, które miały wpływ na wizerunek firmy. Zaprezentowano także dowody na to, jak przerywanie obecności w mediach społecznościowych może zostać mądrze odwrócone poprzez wydajną infuzję treści. Do analizy rezultatów strategii marketingowych spośród wielu narzędzi wybrano "Social Mention", "Talkwalker" i "Mentiolytics", w celu ukazania efektywności i użyteczności metod monitorowania mediów społecznościowych, przy użyciu dostępnych przyborów. Strategia social media marketing Toyoty została wyróżniona w tym badaniu przez przedstawienie pomiarów zmiennych, takich jak zasięg, siła, pasja i sentyment marki na podstawie losowo wybranych okresów, z demonstracją dynamiki ewolucji technik monitoringu mediów społecznościowych.

2

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.

3

Insights into attitudes towards financial innovations by its users

Garškaitė-Milvydienė Kristina, Maknickienė Nijolė, Tvaronavičienė Manuela

Polish Journal of Management Studies

|

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.

4

Sentiment analysis of user preference for old vs new fintech technology using SVM and NB algorithms

Nurdin Tubagus Asep, Alexandri Mohammad Benny, Sumadinata Widya, Arifianti Ria

Management Systems in Production Engineering

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2023

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nr 4 (31)

373--380

EN

The aim of this study is to use sentiment analysis to compare the efficiency of old and new fintech technologies by collecting data from various sources and analyzing it using the SVM and NB algorithms. The study seeks to identify opinions or feelings from text in order to provide a clear picture of public opinion and the direction of the debate regarding old and new fintech technologies. The results of the study show that the SVM algorithm has an average accuracy of 87.32% and the NB algorithm has an average accuracy of 81.56% in testing the sample data in a comparison of old and new fintech technology on the internet. The study tested data in a comparison of two specific arguments, namely the debate about which technology is more efficient in old and new fintech on the internet. Despite many unresolved arguments, the study successfully proved that new fintech is more preferred than old fintech, with 71% positive sentiment directed towards new fintech. However, the dataset also found that 62% negative sentiment is directed towards new fintech, indicating that although new fintech is more preferred, there are still some issues that need to be addressed. One reason for negative sentiment towards new fintech may be the continued concerns about security and privacy of user data. Furthermore, other factors that may cause negative sentiment towards new fintech include a lack of understanding about how the technology works.

5

A model of continual and deep learning for aspect based in sentiment analysis

López Dionis, Artigas-Fuentes Fernando

Journal of Automation Mobile Robotics and Intelligent Systems

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2023

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Vol. 17, No. 1

3--12

EN

Sentiment analysis is a useful tool in several social and business contexts. Aspect sentiment classification is a subtask in sentiment analysis that gives information about features or aspects of people, entities, products, or services present in reviews. Different deep learning models that have been proposed to solve aspect sen‐ timent classification focus on a specific domain such as restaurant, hotel, or laptop reviews. However, there are few proposals for creating a single model with high performance in multiple domains. The continual learn‐ ing approach with neural networks has been used to solve aspect classification in multiple domains. However, avoiding low, aspect classification performance in contin‐ ual learning is challenging. As a consequence, potential neural network weight shifts in the learning process in different domains or datasets. In this paper, a novel aspect sentiment classification approach is proposed. Our approach combines a trans‐ former deep learning technique with a continual learning algorithm in different domains. The input layer used is the pretrained model Bidirectional Encoder Representations from Transformers. The experiments show the efficacy of our proposal with 78 % F1‐macro. Our results improve other approaches from the state‐of-the-art.

6

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

Muralidhar Rathan, Hulipalled Vishwanath R.

Foundations of Computing and Decision Sciences

|

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.

7

Trip planning mobile application: a perspective case study of user experience

Pawełoszek Ilona, Wieczorkowski Jędrzej

Engineering Management in Production and Services

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2023

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Vol. 15, no. 2

55--71

EN

Mobile trip planning applications may contribute to popularising public transport, provided they work efficiently and gain high user acceptance. This article aims to take a closer look at the functioning of the JakDojade application, which has been the most popular platform in Poland for several years, supporting travel planning by public transport. In the presented case study, the authors tried to diagnose problems and indicate the directions of application development. At the same time, through this analysis, the authors aimed to demonstrate the usefulness of researching user comments from the viewpoint of managing the development of mobile applications and related services. A case study methodology was used to perform a descriptive study. Data on user feedback on JakDojade mobile application in Poland comes from Google Play Store. Semantic categorisation of user comments and sentiment analysis allowed for identifying user problems and diagnosing emotions related to its use. The presented methodology allowed for diagnosing typical user problems for the JakDojade application, which may help indicate further development directions. The authors attempted to demonstrate the usefulness of researching user comments from the point of view of managing the development of mobile applications and related services. The semi-automatic approach to text analysis presented in the article highlights the problems related to the study of user reviews. The limitations of the proposed methodology and the possibilities for its improvement were indicated.

8

Handling dimensionality of ambiguity using ensemble classification in social networks to detect multi-label sentiment polarity

Sonawane Sudarshan S., Kolhe Satish R.

Computer Assisted Methods in Engineering and Science

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2023

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Vol. 30, no. 1

7--26

EN

With ever-increasing demand, social media platforms are rapidly developing to enable users to express and share their opinions on a variety of topics. Twitter is one such social media site. This platform enables a comprehensive view of the social media target setting, which may include products, social events, political scenarios, and administrative resolutions. The accessible tweets expressing the target audience’s perspective are frequently impacted by ambiguity caused by natural language processing (NLP) limitations. By classifying tweets according to their sentiment polarity, we can determine whether they express a good or negative point of view, a neutral opinion, or an input tweet that is irrelevant to the sentiment polarity context. Categorizing tweets according to their sentiment can assist future activities within the target domain in constructively evaluating the sentiment polarity and enabling improved decision-making based on the observed sentiment polarity. In this study, tweets that were previously categorized with one of the sentiment polarities were used to conduct predictive analytics of the new tweet to determine its sentiment polarity. The ambiguity of the tweets corpus utilized in the training phase is a critical limitation of the sentiment categorization procedure. While several recent models proposed sentiment classification algorithms, they confined themselves to two labels: positive and negative opinion, oblivious to the plague of ambiguity in the training corpus. In this regard, a novel multi-label classification of sentiment polarity called handling dimensionality of ambiguity using ensemble classification (HAD-EC) method, which diffuses ambiguity and thus minimizes false alerts, is proposed. The experimental assessment validates the HAD-EC approach by comparing the suggested model’s performance to other two existing models.

9

Project success and communication with stakeholders

Targiel Krzysztof S.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska

|

2022

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z. 155

527--534

EN

Purpose: The aim of this paper is to analyze possibilities of using sentiment analysis in project management. Design/methodology/approach: The research methods used in the article were desk research analysis of available source data on the success of project. Then additional research was done on methods of sentiment analysis. Findings: During the course of this work was found a way of use sentiment analysis to improve project management. Research limitations/implications: The proposed idea necessitates research on the verification of the usefulness of the proposed indicators. Practical implications: The indicators proposed in the work have the potential to be used in the project management support application. Originality/value: Novelty of proposed paper are idea of two indicators for improvement project management

10

Analysis of Factors Influencing Developers' Sentiments in Commit Logs: Insights from Applying Sentiment Analysis

Kaur Rajdeep, Chahal Kuljit Kaur, Saini Munish

e-Informatica Software Engineering Journal

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2022

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Vol. 16, nr 1

art. no. 220102

EN

Background: In the open source software paradigm, software development depends upon efforts of volunteer members that are geographically dispersed and collaborate with each other over the Internet. Communication artifacts like mailing lists, forums, and issue tracking systems are used by developers for communication. The way they express themselves through these communication channels greatly influences their productivity, efficiency of development activities, and survival of the project as well. Therefore, it is essential to understand affective state of developers’ contributions to make software engineering more effective. Aim: This study examined commit logs of seven GitHub projects to analyze developers’ sentiments. This study also investigated the relationship of developers’ sentiments in commit logs with team size of project, type of change activity, and contribution volume. Method: Sentiments of developers are calculated using SentiStrength-SE tool that is specialized in software engineering domain. Results: Our findings revealed that the majority of sentiments conveyed by developers in commit logs were neutral. Furthermore, we found that team size, change activity, and commit contribution volume influenced sentiments conveyed in commit logs. Conclusion: Our findings will help project managers to better understand developer sentiments while performing different software development tasks/activities. It will be beneficial in improving developer productivity and retention.

11

Analysis of the Effect of News Sentiment on Stock Market Prices through Event Embedding

Sridhar Sashank, Sanagavarapu Sowmya

Annals of Computer Science and Information Systems

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2021

|

Vol. 25

147--150

EN

Stock market price prediction models have remained a prominent challenge for the investors owing to their volatile nature. The impact of macroeconomic events such as news headlines is studied here using a standard dataset with closing stock price rates for a chosen period by performing sentiment analysis using a Random Forest classifier. A Bi-LSTM time-series forecasting model is constructed to predict the stock prices by using the polarity of the news headlines. It is observed that Random Forest Classifiers predict the polarity of news articles with an accuracy of 84.92%.

12

An evaluation of machine learning and latent semantic analysis in text sentiment classification

Miazga Justyna, Hachaj Tomasz

Technical Transactions

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2020

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Vol. 117, iss. 1

art. no. e2020030

EN

In this paper, we compare the following machine learning methods as classifiers for sentiment analysis: k – nearest neighbours (kNN), artificial neural network (ANN), support vector machine (SVM), random forest. We used a dataset containing 5,000 movie reviews in which 2,500 were marked as positive and 2,500 as negative. We chose 5,189 words which have an influence on sentence sentiment. The dataset was prepared using a term document matrix (TDM) and classical multidimensional scaling (MDS). This is the first time that TDM and MDS have been used to choose the characteristics of text in sentiment analysis. In this case, we decided to examine different indicators of the specific classifier, such as kernel type for SVM and neighbour count in kNN. All calculations were performed in the R language, in the program R Studio v 3.5.2. Our work can be reproduced because all of our data sets and source code are public.

13

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

|

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.

14

Cloud-based sentiment analysis for measuring customer satisfaction in the Moroccan banking sector using Naïve Bayes and Stanford NLP

Riadsolh Anouar, Lasri Imane, ElBelkacemi Mourad

Journal of Automation Mobile Robotics and Intelligent Systems

|

2020

|

Vol. 14, No. 4

64--71

EN

In a world where every day we produce 2.5 quintillion bytes of data, sentiment analysis has been a key for making sense of that data. However, to process huge text data in real-time requires building a data processing pipeline in order to minimize the latency to process data streams. In this paper, we explain and evaluate our proposed real-time customer’ sentiment analysis pipeline on the Moroccan banking sector through data from the web and social network using open-source big data tools such as data ingestion using Apache Kafka, In-memory data processing using Apache Spark, Apache HBase for storing tweets and the satisfaction indicator, and ElasticSearch and Kibana for visualization then NodeJS for building a web application. The performance evaluation of Naïve Bayesian model show that for French Tweets the accuracy has reached 76.19% while for English Tweets the result was unsatisfactory and the resulting accuracy is 56%. To remedy this problem, we used the Stanford core NLP which, for English Tweets, reaches a precision of 80.7%.

15

A dropout predictor system in MOOCs based on neural networks

Mrhar Khaoula, Douimi Otmane, Abik Mounia

Journal of Automation Mobile Robotics and Intelligent Systems

|

2020

|

Vol. 14, No. 4

72--80

EN

Massive open online courses, MOOCs, are a recent phenomenon that has achieved a tremendous media attention in the online education world. Certainly, the MOOCs have brought interest among the learners (given the number of enrolled learners in these courses). Nevertheless, the rate of dropout in MOOCs is very important. Indeed, a limited number of the enrolled learners complete their courses. The high dropout rate in MOOCs is perceived by the educator’s community as one of the most important problems. It’s related to diverse aspects, such as the motivation of the learners, their expectations and the lack of social interactions. However, to solve this problem, it is necessary to predict the likelihood of dropout in order to propose an appropriate intervention for learners at-risk of dropping out their courses. In this paper, we present a dropout predictor model based on a neural network algorithm and sentiment analysis feature that used the clickstream log and forum post data. Our model achieved an average AUC (Area under the curve) as high as 90% and the model with the feature of the learner’s sentiments analysis attained average increase in AUC of 0.5%.

16

An extensive analysis of online restaurant reviews: a case study of the Amazonian Culinary Tourism

Fernandes Junior Luiz Carlos Silva, Silva Junior Jorge, Jacob Junior Antônio, Lobato Fábio

Annals of Computer Science and Information Systems

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2020

|

Vol. 21

81--84

EN

Analyzing User-Generated Content present in social media has become mandatory for companies looking for maintaining competitiveness. These data contain information such as consumer opinions, and recommendations that are seen as rich sources of information for the development of decision support systems. When observing the state of the art, it was found that there is a lack of antecedents that address the analysis of online reviews of Brazilian restaurants. In this sense, the focus of this work is to fill this gap through a case study of Santar\'em city. The results show that professionals in this segment can use these analyzes in order to improve the user's experiences and increase their profits.

17

Tackling the Problem of Class Imbalance in Multi-class Sentiment Classification: An Experimental Study

Lango Mateusz

Foundations of Computing and Decision Sciences

|

2019

|

Vol. 44, No. 2

151--178

EN

Sentiment classification is an important task which gained extensive attention both in academia and in industry. Many issues related to this task such as handling of negation or of sarcastic utterances were analyzed and accordingly addressed in previous works. However, the issue of class imbalance which often compromises the prediction capabilities of learning algorithms was scarcely studied. In this work, we aim to bridge the gap between imbalanced learning and sentiment analysis. An experimental study including twelve imbalanced learning preprocessing methods, four feature representations, and a dozen of datasets, is carried out in order to analyze the usefulness of imbalanced learning methods for sentiment classification. Moreover, the data difficulty factors - commonly studied in imbalanced learning - are investigated on sentiment corpora to evaluate the impact of class imbalance.

18

Analysis of data pre-processing methods for sentiment analysis of reviews

Parlar Tuba, Ozel Selma, Song Fei

Computer Science

|

2019

|

Vol. 20 (1)

123--141

EN

The goals of this study are to analyze the effects of data pre-processing methods for sentiment analysis and determine which of these pre-processing methods (and their combinations) are effective for English as well as for an agglutinative language like Turkish. We also try to answer the research question of whether there are any differences between agglutinative and non-agglutinative languages in terms of pre-processing methods for sentiment analysis. We find that the performance results for the English reviews are generally higher than those for the Turkish reviews due to the differences between the two languages in terms of vocabularies, writing styles, and agglutinative property of the Turkish language.

19

Languages' impact on emotional classification methods

Eilertsen Alexander C., Højbjerg Rose Dennis, Langballe Erichsen Peter, Engesgaard Christensen Rasmus, Pratap Deb Nath Rudra

Annals of Computer Science and Information Systems

|

2019

|

Vol. 18

277--286

EN

There is currently a lack of research concerning whether Emotional Classification (EC) research on a language is applicable to other languages. If this is the case then we can greatly reduce the amount of research needed for different languages. Therefore, we propose a framework to answer the following null hypothesis: The change in classification accuracy for Emotional Classification caused by changing a single preprocessor or classifier is independent of the target language within a significance level of p = 0.05. We test this hypothesis using an English and a Danish data set, and the classification algorithms: Support-Vector Machine, Naive Bayes, and Random Forest. From our statistical test, we got a p-value of 0.12852 and could therefore not reject our hypothesis. Thus, our hypothesis could still be true. More research is therefore needed within the field of cross-language EC in order to benefit EC for different languages.

20

Customer product review summarization over time for competitive intelligence

Amarouche Kamal, Benbrahim Houda, Kassou Ismail

Journal of Automation Mobile Robotics and Intelligent Systems

|

2018

|

Vol. 12, No. 4

70--82

EN

Nowadays, Customer’s product reviews can be widely found on the Web, be it in personal blogs, forums, or ecommerce websites. They contain important products’ information and therefore became a new data source for competitive intelligence. On that account, these reviews need to be analyzed and summarized in order to help the leader of an entity (company, brand, etc.) to make appropriate decisions in an efective way. However, most previous review summarization studies focus on summarizing sentiment distribution toward different product features without taking into account that the real advantages and disadvantages of a product clarify over time. For this reason, in this work we aim to propose a new system for product opinion summarization which depends on the time when reviews are expressed and that covers the sentiments change about product features. The proposed system firstly, generates a summary based on product features in order to give more accurate and efficient information about different features. secondly, classify the product based on its features in its appropriate class (good, medium or bad product) using a fuzzy logic system. The experimental results demonstrate the effectiveness of the proposed system to generate the real image of a product and its features in reviews.