Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)

Dash, Ganesh; Sharma, Chetan; Sharma, Sanjay

doi:10.3390/su15065443

Cited by 24 publications

(7 citation statements)

References 71 publications

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“…It also involves adopting green marketing practices, such as reducing packaging, utilizing eco-friendly materials, and minimizing the carbon footprint associated with product production, distribution, and disposal. Environmental responsibility in marketing extends to advocating for and supporting broader environmental causes, such as conservation efforts, biodiversity, and climate change mitigation [108,109] . Social and environmental responsibilities are interconnected and often overlap in sustainable marketing practices.…”

Section: Marketing and Sustainabilitymentioning

confidence: 99%

Sustainability marketing strategies for the energy sector: Trends, challenges, and future directions

Durmus Senyapar

2024

Environ. Soc. Psychol.

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This study delves into the complex dynamics of sustainable marketing within the energy sector, aiming to unravel the intersection of sustainability principles and marketing strategies that promote environmental stewardship, economic viability, and social equity. Through exploratory qualitative research, the study uncovers critical trends in sustainable marketing, addressing the challenge of aligning strategies with environmental, economic, and social goals. A strategic shift is observed in integrating renewable energy sources, emphasizing environmental benefits and global alignment with sustainability goals. Transparent communication and ethical practices are pivotal, fostering consumer trust and authentic storytelling and establishing a foundation for a trustworthy energy sector. The emphasis on energy efficiency solutions and digital marketing platforms contribute to an informed and empowered consumer base. Customizable energy solutions, incentive programs, collaborative campaigns, and sustainable branding enhance market appeal, reinforcing economic benefits and positioning companies as leaders in transitioning to a sustainable energy future. The critical findings highlight integrating renewable energy sources, a shift towards transparency and ethical marketing practices, promoting energy efficiency solutions, leveraging digital platforms, offering customizable solutions, and engaging in collaborative campaigns with sustainable branding. Synthesizing these findings, the study provides a holistic perspective on strategies contributing to a resilient and eco-conscious future in the energy sector. Energy companies can leverage these insights to refine marketing strategies, align with global sustainability goals, foster consumer trust, and position themselves as leaders in transitioning to a sustainable energy future.

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Section: Marketing and Sustainabilitymentioning

confidence: 99%

Sustainability marketing strategies for the energy sector: Trends, challenges, and future directions

Durmus Senyapar

2024

Environ. Soc. Psychol.

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“…As a result, it was imperative to structure this information coherently, facilitating its comprehension and further analysis. NLP [26,27] was employed to extract specific information from the recognized text. This involved structuring the extracted data to identify key elements such as the organization's Tax Identification Number (TIN), taxpayer identification number, license number, cost of medical services (sometimes written in words or digits), and dates of medical service payments (also variably represented in words or digits), which poses several challenges.…”

Section: Nlp-based Text Structurizationmentioning

confidence: 99%

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

Malashin,

Masich,

Tynchenko

et al. 2024

MAKE

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This study presents an integrated approach for automatically extracting and structuring information from medical reports, captured as scanned documents or photographs, through a combination of image recognition and natural language processing (NLP) techniques like named entity recognition (NER). The primary aim was to develop an adaptive model for efficient text extraction from medical report images. This involved utilizing a genetic algorithm (GA) to fine-tune optical character recognition (OCR) hyperparameters, ensuring maximal text extraction length, followed by NER processing to categorize the extracted information into required entities, adjusting parameters if entities were not correctly extracted based on manual annotations. Despite the diverse formats of medical report images in the dataset, all in Russian, this serves as a conceptual example of information extraction (IE) that can be easily extended to other languages.

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“…BERT (Bidirectional Encoder Representations from Transformers) [35][36][37] embeddings capture rich contextual information and have revolutionized NLP tasks such as text classification, named entity recognition, and sentiment analysis. BOW and TF-IDF [38] embeddings are simpler but still useful for tasks such as document classification or information retrieval, where word frequency or presence is crucial. In our framework, three sentence embeddings were constructed for the source sentence and both translations.…”

Section: Sentence Embeddingsmentioning

confidence: 99%

Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation

et al. 2023

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This paper proposes a hybrid machine translation (HMT) system that improves the quality of neural machine translation (NMT) by incorporating statistical machine translation (SMT). Therefore, two NMT systems and two SMT systems were built for the Slovenian–English language pair, each for translation in one direction. We used a multilingual language model to embed the source sentence and translations into the same vector space. From each vector, we extracted features based on the distances and similarities calculated between the source sentence and the NMT translation, and between the source sentence and the SMT translation. To select the best possible translation, we used several well-known classifiers to predict which translation system generated a better translation of the source sentence. The proposed method of combining SMT and NMT in the hybrid system is novel. Our framework is language-independent and can be applied to other languages supported by the multilingual language model. Our experiment involved empirical applications. We compared the performance of the classifiers, and the results demonstrate that our proposed HMT system achieved notable improvements in the BLEU score, with an increase of 1.5 points and 10.9 points for both translation directions, respectively.

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Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)

Cited by 24 publications

References 71 publications

Sustainability marketing strategies for the energy sector: Trends, challenges, and future directions

Sustainability marketing strategies for the energy sector: Trends, challenges, and future directions

Image Text Extraction and Natural Language Processing of Unstructured Data from Medical Reports

Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation

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