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1 – 10 of 507
Article
Publication date: 29 January 2024

Kai Wang

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among…

Abstract

Purpose

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

Design/methodology/approach

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

Findings

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

Originality/value

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

Details

Data Technologies and Applications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9288

Keywords

Article
Publication date: 18 October 2021

Venkatesh Naramula and Kalaivania A.

This paper aims to focus on extracting aspect terms on mobile phone (iPhone and Samsung) tweets using NLTK techniques on multiple aspect extraction is one of the challenges. Then…

Abstract

Purpose

This paper aims to focus on extracting aspect terms on mobile phone (iPhone and Samsung) tweets using NLTK techniques on multiple aspect extraction is one of the challenges. Then, also machine learning techniques are used that can be trained on supervised strategies to predict and classify sentiment present in mobile phone tweets. This paper also presents the proposed architecture for the extraction of aspect terms and sentiment polarity from customer tweets.

Design/methodology/approach

In the aspect-based sentiment analysis aspect, term extraction is one of the key challenges where different aspects are extracted from online user-generated content. This study focuses on customer tweets/reviews on different mobile products which is an important form of opinionated content by looking at different aspects. Different deep learning techniques are used to extract all aspects from customer tweets which are extracted using Twitter API.

Findings

The comparison of the results with traditional machine learning methods such as random forest algorithm, K-nearest neighbour and support vector machine using two data sets iPhone tweets and Samsung tweets have been presented for better accuracy.

Originality/value

In this paper, the authors have focused on extracting aspect terms on mobile phone (iPhone and Samsung) tweets using NLTK techniques on multi-aspect extraction is one of the challenges. Then, also machine learning techniques are used that can be trained on supervised strategies to predict and classify sentiment present in mobile phone tweets. This paper also presents the proposed architecture for the extraction of aspect terms and sentiment polarity from customer tweets.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 19 February 2024

Tauqeer Saleem, Ussama Yaqub and Salma Zaman

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of…

Abstract

Purpose

The present study distinguishes itself by pioneering an innovative framework that integrates key elements of prospect theory and the fundamental principles of electronic word of mouth (EWOM) to forecast Bitcoin/USD price fluctuations using Twitter sentiment analysis.

Design/methodology/approach

We utilized Twitter data as our primary data source. We meticulously collected a dataset consisting of over 3 million tweets spanning a nine-year period, from 2013 to 2022, covering a total of 3,215 days with an average daily tweet count of 1,000. The tweets were identified by utilizing the “bitcoin” and/or “btc” keywords through the snscrape python library. Diverging from conventional approaches, we introduce four distinct variables, encompassing normalized positive and negative sentiment scores as well as sentiment variance. These refinements markedly enhance sentiment analysis within the sphere of financial risk management.

Findings

Our findings highlight the substantial impact of negative sentiments in driving Bitcoin price declines, in contrast to the role of positive sentiments in facilitating price upswings. These results underscore the critical importance of continuous, real-time monitoring of negative sentiment shifts within the cryptocurrency market.

Practical implications

Our study holds substantial significance for both risk managers and investors, providing a crucial tool for well-informed decision-making in the cryptocurrency market. The implications drawn from our study hold notable relevance for financial risk management.

Originality/value

We present an innovative framework combining prospect theory and core principles of EWOM to predict Bitcoin price fluctuations through analysis of Twitter sentiment. Unlike conventional methods, we incorporate distinct positive and negative sentiment scores instead of relying solely on a single compound score. Notably, our pioneering sentiment analysis framework dissects sentiment into separate positive and negative components, advancing our comprehension of market sentiment dynamics. Furthermore, it equips financial institutions and investors with a more detailed and actionable insight into the risks associated not only with Bitcoin but also with other assets influenced by sentiment-driven market dynamics.

Details

The Journal of Risk Finance, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1526-5943

Keywords

Open Access
Article
Publication date: 29 June 2022

Ibtissam Touahri

This paper purposed a multi-facet sentiment analysis system.

Abstract

Purpose

This paper purposed a multi-facet sentiment analysis system.

Design/methodology/approach

Hence, This paper uses multidomain resources to build a sentiment analysis system. The manual lexicon based features that are extracted from the resources are fed into a machine learning classifier to compare their performance afterward. The manual lexicon is replaced with a custom BOW to deal with its time consuming construction. To help the system run faster and make the model interpretable, this will be performed by employing different existing and custom approaches such as term occurrence, information gain, principal component analysis, semantic clustering, and POS tagging filters.

Findings

The proposed system featured by lexicon extraction automation and characteristics size optimization proved its efficiency when applied to multidomain and benchmark datasets by reaching 93.59% accuracy which makes it competitive to the state-of-the-art systems.

Originality/value

The construction of a custom BOW. Optimizing features based on existing and custom feature selection and clustering approaches.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 4 December 2023

GuangMeng Ji, Siew Imm Ng, Jun-Hwa Cheah and Wei-Chong Choo

Past research often relies on linear relationship assumptions from the perspective of managers when studying the relationship between attribute performance and satisfaction…

Abstract

Purpose

Past research often relies on linear relationship assumptions from the perspective of managers when studying the relationship between attribute performance and satisfaction. However, this study extracts tourists’ online reviews to explore asymmetric relationships and identifies island tourism satisfiers, hybrids and dissatisfiers.

Design/methodology/approach

The research uses 3,523 reviews from Tripadvisor to examine Langkawi Island’s tourist satisfaction. Latent Dirichlet allocation (LDA) machine-learning approach, penalty–reward contrast analysis and asymmetric impact-performance analysis (AIPA) were employed to extract and analyse the data.

Findings

Langkawi’s dissatisfiers included “hotel and restaurant”, “beach leisure”, “water sport”, “snorkelling”, “commanding view”, “waterfall”, “sky bridge walk”, “animal show”, “animal feeding”, “history culture”, “village activity” and “duty-free mall”. Amongst these, five were low performers. Hybrids encompassed “ticket purchasing”, “amenity” “traditional food market” and “gift and souvenir”, all of which were low performers. Only one attribute was categorised as a satisfier: “nature view” which performed exceptionally well.

Practical implications

This study provides recommendations to enhance tourist satisfaction and address tourist dissatisfaction. The elements requiring immediate attention for enhancement are the five low-performance dissatisfiers, as they represent tourists’ fundamental expectations. Conversely, the satisfier or excitement factor (i.e. nature views – mangroves and wildlife) could be prominently featured in promotional materials.

Originality/value

This research constitutes an early endeavour to categorise attributes of island tourism into groups of satisfaction, hybrid or dissatisfaction based on user-generated data. It is underpinned by two-factor and three-factor theories.

Details

Journal of Hospitality and Tourism Insights, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9792

Keywords

Article
Publication date: 5 December 2023

Valeriia Baklanova, Aleksei Kurkin and Tamara Teplova

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the…

Abstract

Purpose

The primary objective of this research is to provide a precise interpretation of the constructed machine learning model and produce definitive summaries that can evaluate the influence of investor sentiment on the overall sales of non-fungible token (NFT) assets. To achieve this objective, the NFT hype index was constructed as well as several approaches of XAI were employed to interpret Black Box models and assess the magnitude and direction of the impact of the features used.

Design/methodology/approach

The research paper involved the construction of a sentiment index termed the NFT hype index, which aims to measure the influence of market actors within the NFT industry. This index was created by analyzing written content posted by 62 high-profile individuals and opinion leaders on the social media platform Twitter. The authors collected posts from the Twitter accounts that were afterward classified by tonality with a help of natural language processing model VADER. Then the machine learning methods and XAI approaches (feature importance, permutation importance and SHAP) were applied to explain the obtained results.

Findings

The built index was subjected to rigorous analysis using the gradient boosting regressor model and explainable AI techniques, which confirmed its significant explanatory power. Remarkably, the NFT hype index exhibited a higher degree of predictive accuracy compared to the well-known sentiment indices.

Practical implications

The NFT hype index, constructed from Twitter textual data, functions as an innovative, sentiment-based indicator for investment decision-making in the NFT market. It offers investors unique insights into the market sentiment that can be used alongside conventional financial analysis techniques to enhance risk management, portfolio optimization and overall investment outcomes within the rapidly evolving NFT ecosystem. Thus, the index plays a crucial role in facilitating well-informed, data-driven investment decisions and ensuring a competitive edge in the digital assets market.

Originality/value

The authors developed a novel index of investor interest for NFT assets (NFT hype index) based on text messages posted by market influencers and compared it to conventional sentiment indices in terms of their explanatory power. With the application of explainable AI, it was shown that sentiment indices may perform as significant predictors for NFT sales and that the NFT hype index works best among all sentiment indices considered.

Details

China Finance Review International, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2044-1398

Keywords

Open Access
Article
Publication date: 16 November 2023

Bahareh Farhoudinia, Selcen Ozturkcan and Nihat Kasap

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information…

1107

Abstract

Purpose

This paper aims to conduct an interdisciplinary systematic literature review (SLR) of fake news research and to advance the socio-technical understanding of digital information practices and platforms in business and management studies.

Design/methodology/approach

The paper applies a focused, SLR method to analyze articles on fake news in business and management journals from 2010 to 2020.

Findings

The paper analyzes the definition, theoretical frameworks, methods and research gaps of fake news in the business and management domains. It also identifies some promising research opportunities for future scholars.

Practical implications

The paper offers practical implications for various stakeholders who are affected by or involved in fake news dissemination, such as brands, consumers and policymakers. It provides recommendations to cope with the challenges and risks of fake news.

Social implications

The paper discusses the social consequences and future threats of fake news, especially in relation to social networking and social media. It calls for more awareness and responsibility from online communities to prevent and combat fake news.

Originality/value

The paper contributes to the literature on information management by showing the importance and consequences of fake news sharing for societies. It is among the frontier systematic reviews in the field that covers studies from different disciplines and focuses on business and management studies.

Details

Aslib Journal of Information Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2050-3806

Keywords

Open Access
Article
Publication date: 14 February 2022

Mohammad Fraiwan

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and…

1466

Abstract

Purpose

Social networks (SNs) have recently evolved from a means of connecting people to becoming a tool for social engineering, radicalization, dissemination of propaganda and recruitment of terrorists. It is no secret that the majority of the Islamic State in Iraq and Syria (ISIS) members are Arabic speakers, and even the non-Arabs adopt Arabic nicknames. However, the majority of the literature researching the subject deals with non-Arabic languages. Moreover, the features involved in identifying radical Islamic content are shallow and the search or classification terms are common in daily chatter among people of the region. The authors aim at distinguishing normal conversation, influenced by the role religion plays in daily life, from terror-related content.

Design/methodology/approach

This article presents the authors' experience and the results of collecting, analyzing and classifying Twitter data from affiliated members of ISIS, as well as sympathizers. The authors used artificial intelligence (AI) and machine learning classification algorithms to categorize the tweets, as terror-related, generic religious, and unrelated.

Findings

The authors report the classification accuracy of the K-nearest neighbor (KNN), Bernoulli Naive Bayes (BNN) and support vector machine (SVM) [one-against-all (OAA) and all-against-all (AAA)] algorithms. The authors achieved a high classification F1 score of 83\%. The work in this paper will hopefully aid more accurate classification of radical content.

Originality/value

In this paper, the authors have collected and analyzed thousands of tweets advocating and promoting ISIS. The authors have identified many common markers and keywords characteristic of ISIS rhetoric. Moreover, the authors have applied text processing and AI machine learning techniques to classify the tweets into one of three categories: terror-related, non-terror political chatter and news and unrelated data-polluting tweets.

Details

Applied Computing and Informatics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2634-1964

Keywords

Article
Publication date: 2 February 2024

Thien Le, Thanh Ho, Van-Ho Nguyen and Hoanh-Su Le

This study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements…

Abstract

Purpose

This study aims to use the voice of the customer (VoC) strategy to collect user-generated content (UGC) compare customer expectations with reality, make the necessary improvements for the business and create personalized strategies for each customer to maximize revenue, focus on hospitality industry in Vietnam market.

Design/methodology/approach

This study proposes a synthesis of techniques for a deep understanding of the VoC based on online reviews in the hospitality industry. First, 409,054 comments were collected from websites in the hospitality sector. Second, the data will be organized, stored, cleaned, analyzed and evaluated. Next, research using business intelligence (BI) solutions integrating three models, including net promoter score (NPS), graph model and latent Dirichlet allocation (LDA), based on natural language processing (NLP) technique, experiment on Vietnamese and English data to explore the multidimensional voice of customer’s row. Finally, a dashboard system will be implemented to visualize analysis results and recommendations on marketing strategies to improve product and service quality.

Findings

Experimental results allow analysts and managers to “listen to the customer’s voice” accurately and effectively, identify relationships between entities, topics of discussion in favor of positive and negative trends.

Originality/value

The novelty in this study is the integration of three models, including NPS, graph model and LDA. These models are combined based on the BI solution and NLP technique. The study also conducted experiments on both Vietnamese and English languages, which ensures more effective practical application.

Details

Journal of Hospitality and Tourism Insights, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2514-9792

Keywords

Article
Publication date: 12 September 2023

Yunfei Xing, Yuming He and Justin Z. Zhang

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and…

Abstract

Purpose

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Design/methodology/approach

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Findings

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Originality/value

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

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