  Emotion recognition in conversations (ERC) is vital to the advancements of
conversational AI and its applications. Therefore, the development of an
automated ERC model using the concepts of machine learning (ML) would be
beneficial. However, the conversational dialogues present a unique problem
where each dialogue depicts nested emotions that entangle the association
between the emotional feature descriptors and emotion type (or label). This
entanglement that can be multiplied with the presence of data paucity is an
obstacle for a ML model. To overcome this problem, we proposed a novel approach
called Label Digitization with Emotion Binarization (LDEB) that disentangles
the twists by utilizing the text normalization and 7-bit digital encoding
techniques and constructs a meaningful feature space for a ML model to be
trained. We also utilized the publicly available dataset called the
FETA-DailyDialog dataset for feature learning and developed a hierarchical ERC
model using random forest (RF) and artificial neural network (ANN) classifiers.
Simulations showed that the ANN-based ERC model was able to predict emotion
with the best accuracy and precision scores of about 74% and 76%, respectively.
Simulations also showed that the ANN-model could reach a training accuracy
score of about 98% with 60 epochs. On the other hand, the RF-based ERC model
was able to predict emotions with the best accuracy and precision scores of
about 78% and 75%, respectively.
