Word-Graph Construction Techniques for Context Analysis

A Nomo-Word Graph Construction Analysis Method (NWGC-AM) is used to graph let the corresponding construction phrases into essential and non-essential citation groups. NMCS-NR, or Nomo Maximum Common Sub-graph edge resemblance, Maximum Common Subgraph Directed Edge resemblance (MCS-DER), and Maximum Common Subgraph Resemblance. The graph resemblance metrics used in this work are called Undirected Edges Resemblance (MCS-UER). The tests included five distinct classifiers: Random Forest, Naive Bayes, K-Nearest Neighbors (KNN), Decision Trees, and Support Vector Machines (SVM).Four sixty one (361) citations made up the annotated dataset used for the studies.  The Decision Tree classifier exhibits superior performance, attaining an accuracy rate of 0.98

Understanding Public Opinions on Social Media about ChatGPT – A deep Learning Approach for Sentiment Analysis

User-generated multimedia content—photos, text, videos, and audio—is becoming more and more common on social networking sites to allow individuals to express their thoughts. One of the largest and most advanced social media platform discussing ChatGPT is Twitter. This is because Twitter updates are constantly being produced and have a limited duration. The deep learning method for sentiment analysis of Twitter data about ChatGPT evaluation is presented in this research. This study used 4-class labels (sadness, joy, fear, and anger) from public Twitter data stored in the Kaggle database. The proposed deep learning strategy significantly improves the efficiency metrics determined by the use of the attention layer in current LSTM-RNN approaches, increasing accuracy by 20% and precision by 10-12%, but recall only 12-13%. Out of 18000 ChatGPT-related tweets, positive, neutral, and negative sentiments accounted for a respective 45%, 30%, and 35%. It is determined that the suggested deep learning technique for ChatGPT review sentiment categorization is effective, realistic, and fast to deploy