Analysis of Machine Learning and Deep Learning Models for Sentiment Analysis of Deepfake Posts

Overview of Model Outcomes

This section describes the outcomes of several ML and DL models for deepfake posts’ sentiment analysis. All these models are tested on a large dataset that is scrapped from X (Twitter). The dataset labeling is performed using TextBlob into positive, negative, and neutral classes. Feature extracting techniques are employed, such as BoW, TF-IDF, word embedding, and a novel TL feature extraction. The performance of diverse ML and DL models is evaluated using a range of metrics. The k-fold cross-validation is also performed to verify the results of different models. All the models are fine-tuned using hyperparameter tuning.

Experimental Design

The experimental design includes the implementation of models using Python programming language with libraries including NLTK, TextBlob, Sklearn, Keras, Pandas, Numpy, TensorFlow, Matplotlib, and Seaborn, among others. Experiments are conducted using the Google Colab platform. Table 3 describes the environment used to conduct the experiments.

Outcomes Using BoW Features

The performance comparison of different ML models with BoW features is represented in Table 4, indicating that LR performs remarkably better than other models with an 87% accuracy.

Outcomes With TF-IDF Features

Table 5 presents the performance comparison of different ML models using the TF-IDF feature, highlighting that the LR model performs better with an 81% accuracy score.

Outcomes With Word Embedding Features

DL models’ performance comparison using the word embedding feature is presented in Table 6, with LSTM and GRU showing the most promising results, achieving a 94% accuracy.

Results With Novel Transfer Features

Table 7 provides a performance comparison of different ML models when using a novel transfer feature, where LR shows outstanding performance with a 97% accuracy.

Results Using Proposed LGR Model

The hybrid LGR model’s performance is shown in Table 8, achieving a 99% accuracy and outperforming previous techniques.

Cross-Validation Results

The comparative evaluation using K-fold cross-validation is shown in Table 9, indicating consistent better performance of the LR model.

Computational Cost Analysis

Table 10 illustrates a computational cost analysis of ML models, revealing that the proposed novel transfer feature technique demonstrates excellent computational efficiency.

Statistical Significance Analysis

Table 11 presents a statistical significance analysis to validate the performance of the proposed LGR approach compared to traditional models.

Error Rate Analysis

Table 12 outlines the error rate analysis, showing significant reductions with the proposed transfer feature.

Ablation Study Analysis

Table 13 evaluates the contribution of the proposed approach, demonstrating that transfer features significantly enhance performance.

State-of-the-Art Comparisons

Table 15 provides a comprehensive evaluation of the proposed approach against state-of-the-art studies, highlighting its superior accuracy for deepfake posts sentiment analysis.

Practical Deployment for Deepfake Content Detection

The practical deployment of the proposed approach aids in tracking emotional reactions and prioritizing harmful content.

Ethical Considerations

This study adheres to ethical research practices, ensuring individual privacy while analyzing sentiment patterns.

Limitations

The study acknowledges limitations such as dataset imbalance and high computational costs of the proposed models.

Future Work

Future research could enhance sentiment analysis accuracy concerning deepfake content by integrating multimodal data and cross-lingual approaches.

Analyzing Deepfake Sentiment with ML and DL Models: Insights and Findings

The rise of deepfake technology has brought both innovative possibilities and significant concerns about the authenticity of online content. Sentiment analysis of such content is crucial for understanding public reactions and implications. In this post, we explore the outcomes of various Machine Learning (ML) and Deep Learning (DL) models for sentiment analysis of deepfake posts, utilizing a comprehensive dataset scraped from Twitter.

Experimental Design

The experimental setup was meticulously designed using Python and key libraries like NLTK, TextBlob, Sklearn, Keras, TensorFlow, and more. We employed Google Colab for our experiments. The data, labeled into positive, negative, and neutral sentiments using TextBlob, serves as the foundation for our analysis.

Key Features

We extracted features using techniques like:

• Bag of Words (BoW)
• TF-IDF (Term Frequency-Inverse Document Frequency)
• Word Embedding
• A novel Transfer Learning (TL) technique

To ensure robust evaluations, we utilized metrics such as accuracy, precision, recall, and F1 score, among others, while also conducting k-fold cross-validation.

Outcomes with BoW Features

Our initial tests focused on BoW features. The performance comparison indicated that Logistic Regression (LR) outperformed other models with an impressive accuracy of 87%. Figures and confusion matrices highlighted the significant differences in performance: while LR excelled, models like Decision Tree (DT) and K-Nearest Neighbors Classifier (KNC) lagged at 78% and 64% accuracy, respectively.

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Outcomes with TF-IDF Features

We then transitioned to TF-IDF features, where LR maintained its edge with an accuracy of 81%. Similarly, DT achieved 80%, while KNC and SVM struggled with lesser accuracy rates of 65% and 48%, respectively. Predictive performance trends mirrored those of BoW, reaffirming LR’s dominance across sentiment classification tasks.

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Word Embedding Features Performance

With the advent of DL, we observed powerful results using word embedding techniques. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) models both reached 94% accuracy, significantly capturing textual intricacies. The Recurrent Neural Network (RNN), while performing well with 93% accuracy, still showed limitations tied to its architecture.

(Insert DL model performance graph here)

Transfer Learning Features

The introduction of novel transfer learning techniques changed the game, pushing LR to achieve an outstanding 97% accuracy. This technique leveraged combined features from LSTM and DT, asserting itself as superior to traditional methods like BoW and TF-IDF.

(Insert transfer learning performance graph here)

The Hybrid LGR Model

Ultimately, we developed a hybrid model combining LSTM, GRU, and RNN with transfer learning features, achieving a remarkable 99% accuracy. This sophisticated architecture demonstrates the potential of integrating multiple techniques to enhance sentiment analysis on deepfake content.

Cross-Validation and Computational Cost

K-fold cross-validation highlighted the robustness of our findings, consistently affirming LR’s performance across various features. Additionally, a computational cost analysis underscored the efficiency of our proposed methods, particularly the novel transfer feature approach, offering high accuracy with minimal training time.

Practical Applications

Our study serves as a foundation for various practical deployments, enabling:

• Monitoring emotional reactions to deepfake content
• Prioritizing harmful content based on sentiment factors
• Developing early warning systems for viral misinformation

Ethical Considerations

Throughout our research, we ensured ethical compliance by anonymizing data and safeguarding user privacy. Understanding the nuances of data representation and potential biases significantly informed our approach.

Limitations and Future Work

While our findings are promising, we acknowledge limitations, such as dataset imbalance and the model’s computational demands for real-time application. Future research could explore integrating visual cues, sarcasm detection, and cross-lingual models to enhance sentiment analysis further.

Conclusion

The landscape of sentiment analysis in deepfake detection is evolving. This research demonstrates significant strides using ML and DL techniques, paving the way for more nuanced and effective methods to tackle the challenges presented by deepfake content. The potential for future development is immense, particularly in the realms of emotional analysis and ethical AI practices.