Dynamic Sentiment Classifier Using Recurrent Neural Networks to Classify Sentiment in Real-Time Across Multiple Languages for Global Markets
DOI:
https://doi.org/10.62647/Keywords:
Sentiment Classifier, Recurrent Neural Networks, LSTM, GRU, Natural Language ProcessingAbstract
The surge of globalization has transformed the market landscape by connecting organizations and people on an international level. A study by the Indian market research firm IMRB indicated that over 70% of Indian consumers rely on online reviews before making purchasing decisions. Furthermore, the exponential growth of the digital landscape, with approximately 600 million Internet users in India, resulted in vast amounts of unstructured data needing analysis. To thrive in this environment, companies must capture customer sentiments on products, services, and trends. Catering to a global audience requires understanding diverse languages and cultural nuances. With the rapid increase in digital data from reviews, surveys, and social media, manual analysis is infeasible. Traditionally, sentiment analysis was conducted manually by experts reviewing customer feedback and social media posts, which was time-consuming and often subjective. To overcome the disadvantages of the former, this project has been introduced. This project proposes a Dynamic Sentiment Classifier utilizing Recurrent Neural Networks, such as LSTM and GRU, optimized to process real-time data across multiple languages. The RNN model captures temporal and contextual relationships, essential for accurately classifying sentiments. Additionally, by leveraging Natural Language Processing (NLP) techniques and multilingual embeddings, the classifier ensures subtle nuances are preserved, making sentiment classification reliable and culturally adaptable. This model equips businesses with actionable insights in real-time, enabling them to fine-tune their strategies and engage effectively with a global audience.
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