Cost-Weighted TF-IDF: A Novel Approach for Measuring Highway Project Similarity Based on Pay Items’ Cost Composition and Term Frequency

Do, Quoc-Khanh; Moriyani, Muhammad Ali; Le, Chau; Le, Tuyen

doi:10.1061/jcemd4.coeng-13023

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…Subsequently, similarity calculations are performed using the TF-IDF method and cosine similarity calculations to measure data similarity. The results of these calculations are utilized in the analysis and modeling process [29]. Next, a predictive model generates job recommendations that align with individual profiles.…”

Section: E Data Analysismentioning

confidence: 99%

Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model

Farell,

Zin Latt,

Jalinus

et al. 2024

JOIV : Int. J. Inform. Visualization

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Vocational high schools are one of the educational stages impacted by Indonesia's low quality of education. Vocational High Schools play a crucial role in improving human resources. Graduates of Vocational High Schools can continue their education at universities or enter the workforce directly. Many students are found to have not yet considered their career path after graduation. At the same time, graduates are still expected to find mismatched employment with their expertise and skills. This research uses CRISP-DM, or Cross Industry Standard Process for Data Mining, to build machine learning models. The approach used is content-based filtering. This model recommends items similar to previously liked or selected items by the user. Item similarity can be calculated based on the features of the items being compared. After students receive job recommendations through intelligent job matching, they can use these recommendations as references when applying for jobs that align with their results. This process helps students direct their steps toward finding jobs that match their profiles, ultimately increasing their chances of success in the job market. These recommendations are crucial in guiding students toward career paths that align with their abilities and interests. The Intelligent Job Matching Model developed in this research provides recommendations for the job-matching process. This model benefits graduates by providing job recommendations aligned with their profiles and offers advantages to the job market. By implementing the Model of Intelligent Job Matching in the recruitment process, applicants with job qualifications can be matched effectively.

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Section: E Data Analysismentioning

confidence: 99%

Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model

Farell,

Zin Latt,

Jalinus

et al. 2024

JOIV : Int. J. Inform. Visualization

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show abstract

“…Sentiment analysis, a pivotal component of this research, harnesses the potency of the Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction technique. TF-IDF serves as a robust methodology for extracting and evaluating the significance of keywords within a given text [6] [7]. Operating on the principle of assigning weights to words based on their frequency in a specific document relative to their occurrence across a broader corpus, TF-IDF provides researchers with a powerful tool to discern words or phrases that recurrently surface in reviews and comments pertaining to electric vehicle incentives.…”

Section: Tf-idf Technology In Text Analysismentioning

confidence: 99%

“…It is essential to note that this research will limit its analysis to reviews, opinions, and public comments related to electric vehicle incentives, excluding technical aspects such as tax considerations or technical regulations. The research will use text-based sentiment analysis methods and TF-IDF feature extraction techniques to understand public sentiment [5][6] [7]. Data sources for this study will be limited to online platforms that capture public opinions regarding electric vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…To achieve the research objectives, data will be collected from various open sources such as social media, review platforms, and discussion forums. This data will be processed using trained sentiment analysis algorithms [8][9] and TF-IDF feature extraction techniques [7]. The research will also utilize Natural Language Processing (NLP) frameworks to identify sentiment patterns.…”

Section: Introductionmentioning

confidence: 99%

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Sentiment Unleashed: Electric Vehicle Incentives Under the Lens of Support Vector Machine and TF-IDF Analysis

2024

J. Appl. Data Sci.

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This research examines public sentiment regarding electric vehicle incentives through sentiment analysis of online comments. These incentives include tax deductions and other financial rewards offered to promote the adoption of electric vehicles. In this study, the researchers collected and analyzed over 1,000 comments from various online platforms to understand the public's perspective on these incentives. The study employs Support Vector Machine (SVM), a powerful machine learning algorithm, as the main method and utilizes Term Frequency-Inverse Document Frequency (TF-IDF) to analyze comment texts. The research findings depict significant variation in public sentiment regarding electric vehicle incentives, with approximately 57.3% of comments expressing negative sentiment, 33.2% positive, and the rest neutral. This study makes a unique contribution to the existing literature by shedding light on the nuanced perspectives of the public, revealing strong support for incentives from a financial standpoint, coupled with notable concerns about electric vehicle prices and charging infrastructure availability. External factors such as government policies and vehicle prices significantly influence public sentiment, and easy access to charging infrastructure plays a crucial role in shaping positive sentiment. Furthermore, the study emphasizes the positive influence of environmental concerns on public support for electric vehicle incentives. The results provide valuable insights into public sentiment, contributing to a better understanding of the factors influencing it, and offer practical policy recommendations for the design and implementation of effective electric vehicle incentives to foster sustainable and environmentally friendly transportation.

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Analyzing long-term parking preferences at Imam Khomeini International Airport: a stated preference approach

Ghaffari,

Mirbaha,

Mirzahossein

et al. 2024

Innov. Infrastruct. Solut.

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Cost-Weighted TF-IDF: A Novel Approach for Measuring Highway Project Similarity Based on Pay Items’ Cost Composition and Term Frequency

Cited by 7 publications

References 37 publications

Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model

Analysis of Job Recommendations in Vocational Education Using the Intelligent Job Matching Model

Sentiment Unleashed: Electric Vehicle Incentives Under the Lens of Support Vector Machine and TF-IDF Analysis

Analyzing long-term parking preferences at Imam Khomeini International Airport: a stated preference approach

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