Extreme Learning Techniques for Enhanced Sentiment Analysis

Abstract

Extreme learning approaches are used to perform sentiment analysis on restaurant evaluations. Sluggish training and overfitting are two problems that traditional supervised learning techniques frequently face. In order to prevent overfitting and speed up training, extreme learning makes use of single hidden layer neural networks with randomly assigned weights. The goal is to create a quick and accurate model for identifying sentiment in meal reviews and to assess the differences between supervised learning techniques and models based on extreme learning. In order to map the scores to attitudes, the study uses a dataset of food reviews, where scores larger than three are interpreted as favourable and scores below that as negative. Training and testing sets are created from the dataset following preparation, which includes handling missing values and choosing pertinent columns. For modeling purposes, text data is transformed into Term Frequency-Inverse Document Frequency (TF-IDF) characteristics. The network is given randomly initialized weights and biases in both single layer and multi layer perceptron implemented. During model training, the loss function is computed, weights and biases are adjusted by backpropagation, and predictions are computed using forward propagation technique. By using a threshold of 0.5, the model's accuracy is assessed. Accuracy scores are used as a statistic for reporting training and testing accuracy. The model further validates the effectiveness for sentiment analysis in the context of food reviews by showing quicker training times and less sensitivity to overfitting. The work presents extreme learning approaches as a competitive substitute for supervised learning, which advances sentiment analysis tools. Comparing the model based on extreme learning to traditional supervised learning methods, experimental results show that the latter achieves competitive accuracy in sentiment analysis. Faster training times and less sensitivity to overfitting are further features. As a strong substitute for supervised learning techniques, this study highlights the effectiveness of extreme learning approaches for sentiment analysis in meal evaluations. Extreme learning improves the efficacy and precision of sentiment analysis models, especially in areas such as restaurant evaluations, furthering the practical uses of sentiment analysis tools.