NUML International Journal of Engineering and Computing

An Information Processing Model for Assessment of User Reviews

2025-09-11T16:06:42+05:00

This Analysis of reviews became a valuable source of accurate information. In this research, we will analyze reviews. Analyzed reviews will create an accurate data set. Fitting an appropriate model is necessary to get accuracy. Problems in fitting an appropriate model are under-fitting and over-fitting. An under-fit model will be less flexible and cannot account for the data. Over-fitting is a modeling error that occurs when a function is too closely fit to a limited set of data points. To solve the mentioned problems, features and appropriate algorithms are selected. As a solution, we will perform preprocessing in a better way with the machine learning algorithms to assess the impact of preprocessing. The main focus of this research is to assess the impact of preprocessing steps on different classifiers.

Evolving Methods in Social Media Sentiment Analysis: Innovations and Challenges

2025-02-04T03:42:54+05:00

Several major studies conducted during the period 2010-2023 have been compiled in this article to illustrate recent progress made toward improving sentiment analysis methods [1] applied to social media websites during that period. In light of the vast amount of user-generated content that is uploaded daily to social media platforms[2], [3] such as Facebook or Instagram, it is essential to evaluate public sentiment on these channels when marketing or monitoring public opinion processes. As a result, traditional machine learning models[4] cannot comprehend the unstructured and evolving language found on social media platforms. For the purpose of improving accuracy, researchers have developed ensemble learning techniques[5] as well as hybrid approaches [6]. CNNs, RNNs, and Transformer models [7] such as BERT have revolutionized sentiment analysis by revealing intricate details. An analysis of real-time sentiment in social media videos can be quickly performed using big data analytics. Multimodal methods [8], [9] incorporating visual, audio, and textual data provide a more comprehensive understanding of sentiments in social media videos. Model adaptability across datasets is improved through transfer learning. Unfortunately, differences in language and ethical issues remain, emphasizing the need for ongoing research to develop adaptable, expandable, ethical sentiment analysis models. The purpose of this review is to discuss the ground-breaking implications of advanced techniques for analyzing social media sentiment, along with future research directions.

A Novel Framework for the Accuracy Enhancement of Facial Expression Recognition System

2025-09-09T07:07:09+05:00

Facial Expression Recognition has become a promising field for more natural interactivity with computing devices and machines and has become the focus of attention for many research scholars over the past decade. Newly developed facial emotions recognition methods focus on neutral expression or six expressions used in most state-of-the-art methods. Accuracy is the main problem in the face recognition results. The problem that must be tackled is the optimization of the expression recognition algorithm i.e. to detect, isolate and correctly translate one of the major expressions of the human face with accuracy targeted towards 100%. This work will try to improve the accuracy of recognizing facial expression by using Histogram of Oriented Gradients (HOG) and Local Ternary Pattern (LTP).