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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Comparative Study Of Opinion Based Classifier Using Speech And Text |
| Authors Name: | Abhishek Rajput , Ayan Yadav , Khushi Singh |
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IJNRD_217744
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| Published Paper Id: | IJNRD2404260 |
| Published In: | Volume 9 Issue 4, April-2024 |
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| Abstract: | - Emotion detection from text and speech is a rapidly evolving field with profound implications for various applications. This comprehensive research paper provides an extensive overview of the latest techniques and advancements, categorizing methodologies based on the data sources they exploit – text, speech, or a strategic combination of both. Traditional machine learning and natural language processing techniques have long underpinned the analysis of textual data, facilitating sentiment analysis and emotion classification across diverse sources like social media and customer reviews. Speech-based emotion detection, on the other hand, relies on auditory signals, delving into vocal features such as pitch, tone, and speech patterns, enabling applications in call center sentiment analysis and voice-activated virtual assistants. The fusion of textual and vocal data offers a more comprehensive understanding of emotion, aided by affective computing techniques like sentiment lexicons and emotional prosody analysis, leading to robust emotion recognition models. Feature extraction, affective lexicons, and contextual analysis play pivotal roles in refining emotion classification. Deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based architectures, have significantly enhanced the precision of emotion detection models, while neural networks for multimodal data fusion allow for simultaneous analysis of text and speech, promising a more holistic perspective in applications like video content and multi-modal conversational data. This overview paper serves as a roadmap for scholars, professionals, and students, aiming to navigate the evolving domain of emotion detection and inspire future innovation in context-aware emotion recognition methods, bridging the gap between human and machine communication in an increasingly digital world. In recent years, emotion detection has transcended its foundational role in sentiment analysis to embrace a more nuanced understanding of human sentiment and emotional states. The incorporation of deep learning techniques, notably Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based architectures, has markedly advanced emotion recognition. These neural networks excel in identifying intricate patterns within text and speech data, enabling the development of more accurate emotion detection models. Moreover, the integration of affective lexicons and contextual analysis has augmented emotion classification precision, demonstrating the vital role of feature extraction in this domain. Speech-based emotion detection, focusing on auditory cues such as pitch and tone, remains invaluable in various applications, including call center sentiment analysis and voice-activated virtual assistants. The fusion of textual and vocal data provides a comprehensive approach, capitalizing on both what is being said and how it is being said, thereby delivering a more robust understanding of emotion. As the boundaries between human and machine interaction continue to blur, this overview paper not only encapsulates the current state of research but also fuels the momentum for further inquiry, innovation, and refinement in the development of context-aware emotion detection methods that bridge the gap between human and machine communication in our increasingly digital world. |
| Keywords: | Emotional Recognition , Test Analysis , Speech Analysis |
| Cite Article: | "Comparative Study Of Opinion Based Classifier Using Speech And Text", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.c273-c280, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404260.pdf |
| Downloads: | 00041 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2404260 Registration ID: 217744 Published In: Volume 9 Issue 4, April-2024 DOI (Digital Object Identifier): Page No: c273-c280 Country: Delhi, Delhi, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2404260 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2404260 |
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