Emotion classification or emotion analysis is the natural language processing (NLP) technique that utilizes the technology to automatically classify human emotion based on the emotional tendency in their text. Emotion classification in conversational text focuses on each expression from the preceding text by considering discourse relationships, identifying the emotions, and classifying the text correspondingly.

  Classic emotion classification methods are not suitable to classify the emotion from conversational text due to their time constraints, human intervention, and a lot of structured training data to achieve an accurate classification. The deep learning model owns the ability to classify emotions through accurate training using superior quality datasets. Deep learning-based emotion classification for conversational text data sequentially analyzing the discussion of text using deep neural architectures and providing effective emotion classification.

  • Emotion classification from the text is inherently a content-based classification problem in Natural Language Processing (NLP).

  • Social media sites generate different textual data and perform an increasingly significant emotional understanding role in emotion classification.

  • Text-based emotion classification makes computers emotionally intelligent to comprehend the human language text.

  • Deep learning models are utilized for the emotion classification from the text by considering the syntactic and semantic features of the text.

  • More recently, a hybrid approach emerged to extract and analyze emotions with the most effective deep learning techniques to enhance performance for emotion classification.

  • Attention network with deep learning is the expected research focus for emotion classification in conversational text, as it provides intelligible comprehensibility and classification of extracted features.