Empowering Customer Feedback Analysis through LSTM-Enhanced Natural Language Processing on Audio Recordings

Abstract

Social media platforms such as Twitter, Facebook, blogs and others act as huge repositories of global information, contributing to the increase in the amount of data including images/video, sound and text due to the consumption of social media and e-commerce websites for large application Despite the proposed techniques, challenges remain, especially in managing large volumes of audio recordings. The on-going challenge of accurate polarity detection in consumer surveys is complicated by extracting accurate meaning from audio data including surveys, voice messages, comments, tweets, and posts, which has to do with sentiment analysis matching audio data The method includes data collection, pre-processing, feature coding , and segmentation steps. The importance of appropriate data collection, pre-processing, and classification analysis is emphasized when interpreting such data. Audio datasets were used to evaluate the effectiveness of the proposed models. The proposed sensitivity prediction method exhibits superior or comparable results with reduced computational complexity. The results not only highlight the critical importance of sentiment analysis in deriving meaningful insights from audio data in consumer research and social media but also explore the integration of LSTM algorithms with captured audio into a rope, thus initiating a new phase of sensory analysis research. Furthermore, accuracy rates were examined for sensitivity analysis in diversity data sets. The Amazon Fine Food dataset achieved 98.5% accuracy, while the Phones & Accessories dataset achieved 98% accuracy. Additionally, the Amazon product dataset achieved 96% accuracy. These high accuracy rates highlight the effectiveness of the sensitivity analysis methodology used, especially in reviewing various items.