Deep Knowledge-Guided Sentiment Prediction using Content Contextual Learning

Registration ID: IJNRD_219635

Published ID: IJNRD2405227

DOI: Click Here to Get

Mulaka Sivarami Reddy , Dr.V. Gowri , Mannam Bhanuprakash , Karanam Manoj Kumar

Sentiment Analysis, Convolution Neural Network, Long Short-Term Memory.

Sentiment analysis on platforms like Twitter and Facebook is crucial for understanding user opinions and preferences. Despite its importance, the accuracy of sentiment analysis is often hindered by the complexities of natural language processing (NLP). Deep learning models, which outperform traditional statistical and lexical approaches, play a key role in advancing NLP tasks. An essential component of these models is word embedding, which helps in generating input features. There are various models for word embedding that cater to both classic and contextual text representations. In this paper, we conduct a comparative study of the most commonly used word embedding techniques, both in their trained and pretrained forms, covering both classical and contextualized methods. Current sentiment classification strategies, especially those for short texts, tend to increase the feature space by incorporating an external open knowledge base. Yet, many of these strategies depend on extensive training data to build the model, leading to high data collection costs and suboptimal learning performance. We propose that there is a strong correlation between knowledge and text labels, and that leveraging this knowledge explicitly could enhance the efficiency of text classification.

Published Paper   E-Certificate

Mulaka Sivarami Reddy, Dr.V. Gowri, Mannam Bhanuprakash, & Karanam Manoj Kumar (May-2024). Deep Knowledge-Guided Sentiment Prediction using Content Contextual Learning. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(5), c243-c249. https://ijnrd.org/papers/IJNRD2405227.pdf

Volume 9 Issue 5, May-2024

Pages : c243-c249

Paper Reg. ID: IJNRD_219635

Published Paper Id: IJNRD2405227

Downloads: 000121975

Research Area: Computer Science & Technology 

Country: Kancheepuram, Tamilnadu, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2405227.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2405227

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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