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<p>Emotion recognition from textual data has gained substantial importance across diverse domains, including sentiment analysis, mental health monitoring, market analytics, and education. The rapid growth of digital textual data from social media, customer feedback, and other sources necessitates the development of effective computational models for identifying emotions. This research explores various word embedding techniques utilizing the ISEAR dataset as a benchmark for emotion detection task. To enhance classification performance, a Convolutional Neural Network (CNN) is integrated due to its proven capability to identify local patterns and its effectiveness in text processing tasks. Preliminary findings reveal distinct differences in performance across embedding methods, with certain approaches demonstrating superior potential. This work contributes to advancing emotion detection by providing a comparative evaluation of embeddings and setting a foundation for optimizing accuracy in NLP applications.</p>

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Deciphering Emotions from Text: A Comparative Analysis of Word Embeddings

  • 期刊论文

  • 发表日期：

    2026年3月17日

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  • Anil Kumar Jadon,
  • Suresh Kumar

摘要

Emotion recognition from textual data has gained substantial importance across diverse domains, including sentiment analysis, mental health monitoring, market analytics, and education. The rapid growth of digital textual data from social media, customer feedback, and other sources necessitates the development of effective computational models for identifying emotions. This research explores various word embedding techniques utilizing the ISEAR dataset as a benchmark for emotion detection task. To enhance classification performance, a Convolutional Neural Network (CNN) is integrated due to its proven capability to identify local patterns and its effectiveness in text processing tasks. Preliminary findings reveal distinct differences in performance across embedding methods, with certain approaches demonstrating superior potential. This work contributes to advancing emotion detection by providing a comparative evaluation of embeddings and setting a foundation for optimizing accuracy in NLP applications.