Sentiment Analysis of Amazon Mobile Reviews Using Deep Learning Techniques for Brand Performance Evaluation
TL;DR
The main goal of this research is to categorize consumer attitudes into three groups: neutral, negative, and positive, and the outcomes show that deep learning models perform better and produce the best results.
Abstract
Nowadays, Natural Language Processing, or NLP, is a key component of many programs that analyze and comprehend human language. The sentiment analysis of mobile product reviews collected from the Kaggle repository—more especially, the 20,710-review Amazon Mobile evaluations dataset—is the main emphasis of this research. Reviews of well-known cellphone companies including Samsung, Nokia, Apple, Redmi, and others are included in the dataset. The main goal of this research is to categorize consumer attitudes into three groups: neutral, negative, and positive. This research heavily relies on Natural Language Processing (NLP), particularly in the commercial and e-commerce domains where decision-making and product enhancement depend on a comprehension of client input. In this research, sentiment categorization is carried out using deep learning methods like Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), and Convolutional Neural Network (CNN). Lowercase conversion, punctuation and symbol removal, tokenization, stopword removal, stemming, and lemmatization are some of the preprocessing methods used to enhance text quality and model performance. The algorithms are compared based on execution time and accuracy. The survey also determines the top-performing mobile brand by counting the amount of positive reviews. The outcomes show that deep learning models perform better and produce the best results. This research promotes business intelligence in the e-commerce industry and advances our understanding of consumer sentiment behavior.