Multimodal Content Analysis Using Deep Learning

Abstract

The multimodal content analysis platform combines sentiment analysis and neural style transfer techniques to process and improve various types of digital content. The sentiment analysis module utilizes natural language processing (NLP) algorithms, such as recurrent neural networks (RNNs) or transformer models like BERT, to extract emotional signals from textual, visual, and auditory inputs. Signals are classified into predefined sentiment categories, providing granular insights into the emotional context of the content. The platform employs neural style transfer algorithms, such as style transfer networks (NSTNs) or generative adversarial networks (GANs), to transfer stylistic attributes between texts. By training on a diverse range of artistic styles, the system learns to apply these styles to input text while preserving semantic meaning. This process enhances the visual representation of textual content, making it more appealing and engaging to users.

Introduction

I. INTRODUCTION

Multimodal Content Analysis Platform features a style text transfer project that leverages cutting-edge deep learning algorithms to apply artistic styles to textual content. By learning from a diverse range of artistic styles, the platform transforming plain text into visually stunning designs captivating representations, enhancing its aesthetic appeal and audience engagement. This groundbreaking strategy not only enriches the visual presentation of textual content but also enables users to convey complex ideas and emotions in a more compelling manner.

The platform starts integrating two groundbreaking projects – sentiment analysis and style text transfer – Multimodal Content Analysis Platform offers users a comprehensive toolkit to decode audience sentiments and preferences, while also transforming textual content into visually captivating representations. In this paper, we embark on a journey to explore the architecture, functionalities, and applications of Multimodal Content Analysis Platform, demonstrating its capacity to reshape the landscape of multimedia content creation and optimization. Join us as we delve into the transformative capabilities of Multimodal Content Analysis Platform and its implications for the future of digital communication and engagement.

The platform stands at the crossroads of cutting-edge machine learning and multimedia processing, offering a comprehensive suite of tools to analyze and elevate digital content across diverse modalities. By harnessing the power of natural language processing (NLP) and deep learning, we empower users to delve deep into the emotional nuances of textual, visual, and auditory inputs. Furthermore, through state- of-the-art style transfer algorithms, our platform enables users to imbue textual content with captivating artistic styles, enriching its visual presentation and fostering deeper audience engagement.

II. LITERATURE REVIEW

III. METHODOLOGY

The provided block diagram outlines the workflow of a Multimodal content analysis[MMCA] platform utilizing Deep learning and CNN. The process begins with users opening the webpage that shows a title of sentiment analysis. Upon opening, they are two options to choose, by the users gain access to either of one, which takes the users for further process.

With the selection of either sentimental analysis or style transfer, it proceeds to next page if the user choice is sentimental analysis the shows up three options: image, text, audio, for image we get an upload option where selected image is processed and it shows whether it is happy, sad or neutral as output, similarly in audio we upload a file and based on the file played it identifies whether the audio played is happy, sad or depressed etc.

If the user selects text, then it provides us some input and then analyses it the output shows us whether the statement is positive or negative, confidence score, subjectivity and polarity.

If the user selects the style option then, two options are provided for a text given by the user as formal to informal or informal to formal. This block diagram encapsulates the seamless integration of multimodality, style transfer and interaction to facilitate a multiple modal as one.

In the surveyed paper 2023 [4], we have identified areas of lag and are currently in the process of    implementing additional features to address these shortcomings. In addition to addressing the identified shortcomings, we are also incorporating new features to overcome the shortcomings

1. Evaluation Metrics: The model is evaluated using specific metrics, including formality and meaning preservation, to assess the quality of the style transfer process.

Potential Impact: By providing a practical solution for informal-to-formal style transfer,  project has the  potential  to positively  impact  various sectors , including education , business communication, and professional writing, by improving the clarity, professionalism, and effectiveness of written communication

V. ACKNOWLEDGEMENT

The satisfactions that accompany the successful completion of project on "MULTIMODAL CONTENT ANALYSIS PLATFORM" would be incomplete without the mention of people who made it possible, whose noble gesture, affection, guidance, encouragement and support crowned my efforts with success. It is my privilege to express my gratitude and respect to all those who inspired us in the completion of this work.

We are extremely grateful to our Guide Mr. Reddy Santhosh Kumar Asst Prof of Department of AIML for noble gesture, support, co-ordination and valuable suggestions given in completing the work. We also thank Dr. BM Vidyavathi, H.O.D Department of AIML, for her coordination and valuable suggestions given in completing the work.

Conclusion

The implementation of the Multimodal Content Analysis Platform represents a significant leap forward in the field of multimedia content analysis and optimization. Through the seamless integration of sentiment analysis and neural style text transfer, Multimodal Content Analysis Platform offers content creators, marketers, and researchers a powerful toolkit to decode audience sentiments, enhance content quality, and drive meaningful engagement. Additionally, the platform empowers users to elevate the visual presentation of textual content by applying diverse artistic styles, thereby enhancing its aesthetic appeal and impact.

References

[1] \"Research on Sentiment Analysis Model of Short Text Based on Deep Learning\", by Zhou Gui Zhou Scientific Programming, vol. 2022, Article ID 2681533 in (2022). [2] \"Attention-Based Sentiment Region Importance and Relationship Analysis for Image Sentiment Recognition\" by Shanliang Yang, Linlin Xing, Zheng Chang, Yongming Li, Computational Intelligence and Neuroscience, vol. 2022, Article ID 9772714 in (2022). [3] \"Hybrid Deep Learning Models for Sentiment Analysis\"Cach N. Dang, María N. Moreno-García, Fernando De la Prieta, Complexity, vol. (2021), Article ID 9986920…in (2021). [4] \"Formal styler: GPT-Based Model for Formal Style Transfer with Meaning Preservation Rivero, Mariano & Tirado, Cristhiam & Ugarte, Willy. (2023). SN Computer Science. 4. 10.1007/s429 79-023-02110-7

Copyright

Copyright © 2024 Mr. Madhu Kumar Papti, Mr. Rishabh Kumar Dhirhe, Mr. Srivatsa A, Ms. Suhana Mahek B, Mr. Reddy Santosh Kumar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.