ABSTRACT:

This project presents a hybrid machine learning and deep learning system for sentiment analysis of tweets related to deepfake content. The system integrates multiple models including Logistic Regression, Decision Tree, LSTM, and a Transfer Learning framework that combines outputs of both Decision Tree and LSTM to enhance sentiment prediction accuracy. The dataset, consisting of deepfake-related tweets, is first preprocessed through advanced Natural Language Processing (NLP) techniques such as stopword removal, lemmatization, tokenization, and cleaning of URLs and special characters to ensure high-quality input data.
For sentiment labeling, an ensemble-based Sentiment Model Voting Classifier (SMVC) approach is used, combining three powerful sentiment analyzers: TextBlob, VADER, and AFINN, to generate robust sentiment labels. The features are then extracted using TF-IDF vectorization, creating numerical representations suitable for traditional machine learning models.
The Logistic Regression and Decision Tree classifiers are trained on these TF-IDF features, while a separate LSTM model is trained using tokenized and padded tweet sequences, capturing deep contextual relationships within the text. A novel Transfer Learning method fuses the probabilistic outputs of LSTM and Decision Tree, followed by a Logistic Regression model trained on these combined features to refine the final prediction.
Furthermore, the system supports real-time sentiment analysis through a user-friendly interface, where users can input any tweet to receive sentiment predictions. All models, including vectorizers and scalers, are stored using Pickle and H5 formats, ensuring reusability.
The experimental results demonstrate high accuracy in identifying the polarity of deepfake-related tweets, indicating the potential of combining traditional and deep learning models in handling complex textual data.


INTRODUCTION:

In the rapidly evolving digital era, deepfake technology has emerged as a significant threat to online communication, information integrity, and public trust. Deepfakes, created using advanced artificial intelligence (AI) techniques such as generative adversarial networks (GANs), have enabled the seamless creation of synthetic media where individuals appear to say or do things they never actually did. These synthetic contents are highly convincing and have found applications in malicious domains including misinformation campaigns, identity fraud, and defamation. The alarming growth of deepfake-related content on social media platforms like Twitter has fueled the need for robust detection systems that can discern between genuine and manipulated information. One of the major consequences of deepfake proliferation is its influence on public sentiment and opinion formation. Therefore, analyzing and detecting deepfake tweets not only helps in identifying fake content but also plays a vital role in understanding how public emotions are manipulated. Addressing this issue requires integrating Natural Language Processing (NLP) techniques to examine textual content for suspicious and deceptive patterns.

In this work, we propose a hybrid AI model that combines both Machine Learning (ML) and Deep Learning (DL) techniques to detect the sentiment of deepfake tweets effectively. The system follows a multi-step pipeline that starts with text preprocessing, where raw tweet content is cleaned and normalized by removing noise such as URLs, mentions, hashtags, and punctuation marks. This is followed by feature extraction using the TF-IDF (Term Frequency-Inverse Document Frequency) vectorizer, which converts text into numerical feature vectors that can be fed into ML algorithms. Additionally, tokenization and sequence padding are applied to prepare textual data for LSTM (Long Short-Term Memory) networks, enabling the model to understand contextual and sequential information within tweets.

To ensure reliable sentiment classification, multiple algorithms are deployed, including Logistic Regression, Decision Tree Classifier, and LSTM-based neural networks. Each of these models brings unique strengths to the analysis. Logistic Regression is a robust baseline method suitable for high-dimensional data like TF-IDF vectors. Decision Trees add interpretability and non-linearity to the classification process, allowing the model to handle complex relationships between words. LSTM, a specialized form of Recurrent Neural Network (RNN), is particularly powerful in learning long-term dependencies in sequential data, making it well-suited for natural language processing tasks. The inclusion of LSTM ensures that our system understands the order and context of words, capturing nuances that traditional algorithms might overlook.

Furthermore, to enhance the accuracy and robustness of the detection system, a fusion-based technique is employed where features derived from Decision Tree and LSTM models are combined and fed into a Logistic Regression classifier, creating a transfer learning framework. This integration allows the system to leverage both shallow and deep representations of the tweet text, improving overall prediction performance. The feature fusion not only combines probabilistic insights from Decision Trees but also contextual sequence patterns captured by LSTM, thus forming a holistic representation of the tweet.

The proposed system also incorporates ensemble sentiment analysis by adopting a Sentiment Majority Voting Classifier (SMVC), utilizing the outputs from popular sentiment analysis tools such as TextBlob, VADER, and AFINN. These tools analyze the polarity and subjectivity of the tweet text and contribute their individual sentiment assessments, which are combined via majority voting to assign the final sentiment label. This ensemble approach mitigates the biases and weaknesses of individual tools and provides a balanced sentiment evaluation.

Given the sensitivity and global impact of fake information dissemination, such a detection system holds immense value for social media companies, governments, and cybersecurity agencies aiming to maintain the integrity of online discourse. It also serves as an educational tool for users, increasing awareness about the existence and risks of deepfakes. Moreover, the system’s modular design allows future integration of multimodal data (like images and videos) for even more comprehensive deepfake detection solutions.

This project represents a step forward in the field of AI-based security tools, showcasing the synergistic potential of combining classical ML, advanced DL, and ensemble methods for real-world problems. The experiment results, including accuracy scores and classification reports for each model, demonstrate the effectiveness of the proposed methodology.