BLIP-NLP Model for Sentiment Analysis

Srinivas, P V V S; Gayathri, K.; Bhavitha, Kola; Jahnavi,; Sarath, Kodagoda

doi:10.1109/icecaa58104.2023.10212253

Cited by 2 publications

References 18 publications

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Emerging Trends in Data-Driven Marketing

Behare,

Chaudhari,

Sharma

et al. 2024

Advances in Marketing, Customer Relationship Management, and E-Services

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This chapter aims at exploring the evolving landscape of marketing in view of emerging technologies, consistently changing methodologies and integration of data science and marketing. This chapter will drive in understanding the impact of AI, blockchain, IoT, and AR/VR on marketing strategies, in conjunction with the emerging techniques like sentiment analysis, predictive analytics, and cross-channel integration. Anticipated developments in the field of data driven marketing highlight hyper-personalization, real-time marketing, and ethical considerations, whereas implications encompass data governance, agile marketing, and evolution of necessary skillset. The chapter also emphasizes the significance of uninterrupted innovation and adaptation in the marketing technologies to navigate the complications in the ecosystem of data-driven marketing.

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Emerging Trends in Data-Driven Marketing

Behare,

Chaudhari,

Sharma

et al. 2024

Advances in Marketing, Customer Relationship Management, and E-Services

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Study of the Application of VQA Deep Learning Technology to the Operation and Management of Urban Parks† - Analysis of SNS Images -

Lee,

Park,

Lee

2023

J Korean Inst Landsc Archit

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This research explores the enhancement of park operation and management by analyzing the changing demands of park users. While traditional methods depended on surveys, there has been a recent shift towards utilizing social media data to understand park usage trends. Notably, most research has focused on text data from social media, overlooking the valuable insights from image data. Addressing this gap, our study introduces a novel method of assessing park usage using social media image data and then applies it to actual city park evaluations. A unique image analysis tool, built on Visual Question Answering (VQA) deep learning technology, was developed. This tool revealed specific city park details such as user demographics, behaviors, and locations. Our findings highlight three main points: (1) The VQA-based image analysis tool's validity was proven by matching its results with traditional text analysis outcomes. (2) VQA deep learning technology offers insights like gender, age, and usage time, which aren't accessible from text analysis alone. (3) Using VQA, we derived operational and management strategies for city parks. In conclusion, our VQA-based method offers significant methodological advancements for future park usage studies.

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BLIP-NLP Model for Sentiment Analysis

Cited by 2 publications

References 18 publications

Emerging Trends in Data-Driven Marketing

Emerging Trends in Data-Driven Marketing

Study of the Application of VQA Deep Learning Technology to the Operation and Management of Urban Parks† - Analysis of SNS Images -

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