Audio Sentiment Detection using a Hybrid Analysis Model

Analyzing customer sentiment calls is crucial for organizations to monitor customer satisfaction, identify pain points, and improve service quality. Performing sentiment analysis on audio input, while difficult, is a vital aspect of research in the field of NLP, that can yield valuable insights into the emotional tone and attitudes expressed in spoken language.The SemEval 2016 [1], SemEval 2014 [2], and IMDB [3] datasets contain thousands of annotated reviews that serve as a resource for training and evaluation.This paper proposes a thorough approach for sentiment analysis on audio inputs, emphasizing speech-to-text conversion, text preprocessing, numerical representation using Word2Vec, Count Vectorizer, and TF-IDF techniques, and the development of multiple hybrid models. Each of the models is assessed according to metrics for accuracy, precision, recall, and F1-score. High accuracies of 86.58% for TF-IDF+Naive Bayes, 84.90% for Naive Bayes, 84.42% for CNN+SVM, and 67% for CNN+LSTM are shown in the results, demonstrating the usefulness of the suggested methodology for sentiment prediction tasks.By providing insightful information for applications in domains including opinion mining, social media monitoring, and customer feedback analysis, this research advances sentiment analysis techniques on audio inputs. Future developments in the fields of sentiment analysis and audio processing are outlined, along with additional improvements and expansions of the suggested methodology.