Bidirectional encoder representations from transformers and deep learning model for analyzing smartphone-related tweets - Sudheesh R; Muhammad Mujahid; Furqan Rustam; Bhargav Mallampati; Venkata Chunduri; Isabel de la Torre Díez; Imran Ashraf (PeerJ Computer Science, 2023)
Nearly six billion people globally use smartphones, and reviews about smartphones provide useful feedback concerning important functions, unique characteristics, etc. Social media platforms like Twitter contain a large number of such reviews containing feedback from customers. Conventional methods of analyzing consumer feedback such as business surveys or questionnaires and focus groups demand a tremendous amount of time and resources, however, Twitter’s reviews are unstructured and manual analysis is laborious and time-consuming. Machine learning and deep learning approaches have been applied for sentiment analysis, but classification accuracy is low. This study utilizes a transformer-based BERT model with the appropriate preprocessing pipeline to obtain higher classification accuracy. Tweets extracted using Tweepy SNS scrapper are used for experiments, while fine-tuned machine and deep learning models are also employed. Experimental results demonstrate that the proposed approach can obtain a 99% classification accuracy for three sentiments.