Sentiment Analysis on Automobile Brands Using Twitter Data

Asghar, Zain; Ali, Tahir; Ahmad, Ishtiaque; Tharanidharan, Sridevi; Nazar, Shamim Kamal Abdul; Kamal, Shahid

doi:10.1007/978-981-13-6052-7_7

Cited by 13 publications

(7 citation statements)

References 8 publications

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“…Their research examines users' sentiments to determine if they are happy or sad about using the new iPhones. Using a similar methodology and Twitter big data, Asghar et al [48] also studied people's automobile preferences. Generally, in commerce and marketing, companies use UGC to understand customers' perceptions and satisfaction and how their goods and services are compared with other similar products in the market [49].…”

Section: The Use Of Artificial Intelligence and User-generated Conten...mentioning

confidence: 99%

“…Negative comments from the community residents and visitors represent displeasure and community challenges. Due to the unstructured nature of the social media data, VADER is one of the best NLP tools for analysing sentiments from social media UGC [47,48]. (f, g and h) Survey and Data Validation-VADER is trained and validated by the developers [64], and Abdul-Rahman, Chan, Wong, Irekponor, and Abdul-Rahman [55] showed that the output has high accuracy.…”

Section: (E)mentioning

confidence: 99%

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Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns

et al. 2023

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University towns face many challenges in the 21st century due to urbanization, increased student population, and higher educational institutions’ inability to house all their students on-campus. For university towns to be resilient and sustainable, the challenges facing them must be assessed and addressed. To carry out community resilience assessments, this study adopted a novel methodological framework to harness the power of artificial intelligence and social media big data (user-generated content on Twitter) to carry out remote studies in six university towns on six continents using Text Mining, Machine Learning, and Natural Language Processing. Cultural, social, physical, economic, and institutional and governance community challenges were identified and analyzed from the historical big data and validated using an online expert survey. This study gives a global overview of the challenges university towns experience due to studentification and shows that artificial intelligence can provide an easy, cheap, and more accurate way of conducting community resilience assessments in urban communities. The study also contributes to knowledge of research in the new normal by proving that longitudinal studies can be completed remotely.

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Section: The Use Of Artificial Intelligence and User-generated Conten...mentioning

confidence: 99%

Section: (E)mentioning

confidence: 99%

Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns

et al. 2023

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“…With recent innovations in big data mining and pre-processing through artificial intelligence, identifying community challenges due to studentification has become easier and more accurate [27]. Building on previous works in the area of textual data mining (user-generated contents) from microblogs, machine learning (ML) and natural language processing (NLP) methods for longitudinal studies by Alharbi et al [28], Asghar et al [29], Khan et al [30], Jansen et al [31], Abumalloh et al [32], Carlos et al [33], Shah et al [34], Nilashi et al [35], Sun et al [36], Ahani et al [37], and Ahani et al [38], and Abdul-Rahman, Chan, Wong, Irekponor, and Abdul-Rahman [27] developed a comprehensive mining and pre-processing framework with algorithms that can accurately identify community challenges for the urban planning sector. Since this framework is recent and has a high accuracy level, there is no need to duplicate the effort here.…”

Section: Studentification In Akoka Lagos Nigeriamentioning

confidence: 99%

A Composite Resilience Index (CRI) for Developing Resilience and Sustainability in University Towns

2023

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Globally, most higher educational institutions can no longer house their students within their campuses due to the increased number of enrolments and the unavailability of land for spatial expansion, especially in urban areas. This leads to studentification which negatively impacts university towns. Developing resilience against the negative impacts of studentification will make university towns more sustainable. However, there is no existing community resilience index designed for that purpose. Thus, this study develops a composite resilience index for university towns, using Akoka, a university town in Lagos, Nigeria, as a case study. The composites of the index were determined by prioritizing online user-generated content mined from Twitter between 1 January 2010 and 31 December 2021 using artificial intelligence, while the elements of resilience and risk reduction were developed through the Delphi and analytic hierarchy process. The research outcomes showed that the physical, economic, social, and cultural criteria subjected to comparisons represented ≥70% of the total weights. These criteria made up the outcome indicators, while the integrated community-based risk reduction program model was adopted for the process indicators. Both outcome and process indicators formed the localized composite resilience index for Akoka, Lagos, Nigeria. This proposed composite resilience index would help the town to assess and build resilience against the negative impacts of studentification and provide a methodology for other university towns to create theirs using similar methods.

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“…Lee et al chose Toyota Yaris as the case to study and designed a content analyzer based on co-occurrence analysis to find out the most important elements that the users in MForum care about [35]. Asghar et al analyzed the sentiment of Twitter users towards Honda, Toyota, BMW, Audi and Mercedes using a naive Bayes classification method [36]. Sun et al proposed a method for dynamically analyzing changes in customers' sentiments toward the attributes of Trumpchi GS4 and GS8 [37].…”

Section: Customer Preferences Identification In the Automobile Industrymentioning

confidence: 99%

An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content

Luo,

Song,

Zhou

et al. 2023

Sustainability

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This work attempts to develop a novel framework to reveal the preferences of Chinese car users from online user-generated content (UGC) and guides automotive companies to allocate resources reasonably for sustainable design and improve existing product or service attributes. Specifically, a novel unsupervised word-boundary-identified algorithm for the Chinese language is used to extract domain professional feature words, and a set of sentiment scoring rules is constructed. By matching feature-sentiment word pairs, we calculate car users’ satisfaction with different attributes based on the rules and weigh the importance of attributes using the TF-IDF method, thus constructing an importance-satisfaction gap analysis (ISGA) model. Finally, a case study is used to realize the framework evaluation and analysis of the twenty top-mentioned attributes of a small-sized sedan, and the dynamic ISGA-time model is constructed to analyze the changing trend of the importance of user demand and satisfaction. The results show the priority of resource allocation/adjustment. Fuel consumption and driving experience urgently need resource input and management.

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Sentiment Analysis on Automobile Brands Using Twitter Data

Cited by 13 publications

References 8 publications

Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns

Novel Use of Social Media Big Data and Artificial Intelligence for Community Resilience Assessment (CRA) in University Towns

A Composite Resilience Index (CRI) for Developing Resilience and Sustainability in University Towns

An Analysis Framework to Reveal Automobile Users’ Preferences from Online User-Generated Content

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