Classification Users' Sentiments About The Threads Application Using Machine And Deep Learning

Abstract

Sentiment analysis is a crucial tool in our data-driven world. It is considered Sentiment analysis is an important topic in the field of natural language processing(NLP). With the increasing volume of text data flowing through social media platforms and review sites. Through it, users' opinions and feelings about a specific product or topic can be understood and these comments can be analyzed into negative, good, and neutral. Our study aims to classify the opinions and feelings of twitter users about one of Meta's applications, which is the (Thread) application, using machine learning algorithms and deep learning networks. The dataset was obtained from the largest data collection is from the (Kaggle) website, as this data set contains reviews of application users. Using recurrent neural networks that have shown good results compared to machine learning algorithms. Satisfactory results were obtained according to the evaluation metrics used is (Confusion matrix,F1-score ,Accuracy, Precision  and Recall). This study contributes to the field of natural language processing by providing insights into users' experiences with the Thread app. These findings enable app developers to understand user perceptions and improve their experience. Using sentiment analysis enables companies to understand what customers are saying about their products and services, helping them accurately identify strengths and weaknesses.