Sentiment Analysis of Incoming Voice Calls

This project aims to meet the increasing need for real time sentiment analysis within voice call interactions, acknowledging the rising significance of voice based engagements in today’s telecommunications realm. For instance, pre trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N grams, BERT, and CNN. In the model, sentiment lexicon, N grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets