K. Anusha Reddy, K. Vamshee Krishna, Chinthakindi Koushika, Racherla Harshith, Shivuni Ganindra 03 04(1) The exponential growth of social media has led to the continuous generation of vast amounts of usergenerated textual content necessitating efficient automated techniques for meaningful analysis The release of The Social Dilemma triggered extensive global engagement producing a large volume of tweets that capture varied public perspectives on issues such as digital ethics platform governance and regulatory concerns Traditional manual analysis methods are inadequate for handling such largescale and dynamic datasets due to limitations in time efficiency and scalability To overcome these challenges this study presents an integrated Natural Language Processing NLP framework designed for concurrent sentiment detection and topic categorization The workflow begins with structured preprocessing including tokenization stop word elimination and lemmatization followed by Exploratory Data Analysis EDA to identify underlying textual patterns and distribution trends Contextaware semantic features are extracted using RoBERTabased embeddings enabling a richer representation of textual meaning To address data imbalance issues the Synthetic Minority Oversampling Technique SMOTE is incorporated ensuring fair representation across different classes The processed features are then used to train multiple baseline classifiers such as Decision Tree DT KNearest Neighbor KNN and Nave Bayes NB facilitating comparative performance analysis Furthermore a Deep Neural Network DNN is utilized as a feature extractor and its learned representations are enhanced through a Tree Alternating Optimization TAO Tree Classifier The proposed Social Transform Deep Tree STDT framework effectively categorizes sentiments into Negative Neutral and Positive while also identifying key thematic topics Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026