Unveiling Deception: A Fusion of Deep Learning and Sentiment Analysis for Identifying Counterfeit Reviews
Keywords:
VADER, incorporates, comprehensive, counterfeit, Long Short-Term MemoryAbstract
In the contemporary landscape of consumer decision-making, the influence of online reviews is paramount. However, the authenticity of these reviews has become a pressing concern. This study proposes a comprehensive strategy for identifying counterfeit reviews on online platforms by integrating advanced deep learning techniques with sentiment analysis. The primary objective is to develop a model capable of distinguishing between deceptive and genuine reviews. The methodology includes data acquisition, preprocessing, and the application of a neural network model featuring key elements such as an Embedding layer for word representations, a Convolutional layer for feature extraction, a Long Short-Term Memory (LSTM) layer for capturing sequential dependencies, and a Dense output layer for binary classification. To evaluate the model's effectiveness, a dataset comprising categorized reviews is utilized. The dataset is split into training and testing subsets, and the model undergoes training across multiple epochs, with continuous monitoring of metrics like loss and accuracy. Visual representations illustrate the model's training progress. Additionally, the study incorporates sentiment analysis using the VADER tool to assess the emotional tone of reviews, aiding in the differentiation between authentic and fabricated sentiments. The research findings highlight the efficacy of the combined deep learning and sentiment analysis approach in detecting counterfeit reviews. The model exhibits competitive performance in review classification, potentially enhancing trustworthiness on online platforms. The sentiment analysis component enriches our understanding of user sentiments, providing a deeper insight into review content. By offering a robust and interpretable model alongside a comprehensive methodology, this research significantly contributes to the field of counterfeit review detection in the digital era.
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Kauffmann, E., Peral, J., Gil, D., Ferrández, A., Sellers, R., & Mora, H. (2020). A framework for big data analytics in commercial social networks: A case study on sentiment analysis and fake review detection for marketing decision-making. Industrial Marketing Management, 90, 523-537.
Alsubari, S. N., Deshmukh, S. N., Alqarni, A. A., Alsharif, N., Aldhyani, T. H., Alsaade, F. W., & Khalaf, O. I. (2022). Data analytics for the identification of fake reviews using supervised learning. Computers, Materials & Continua, 70(2), 3189-3204.
Wang, J., Kan, H., Meng, F., Mu, Q., Shi, G., & Xiao, X. (2020). Fake review detection based on multiple feature fusion and rolling collaborative training. IEEE Access, 8, 182625-182639.
Mohawesh, R., Tran, S., Ollington, R., & Xu, S. (2021). Analysis of concept drift in fake reviews detection. Expert Systems with Applications, 169, 114318.
Ruan, N., Deng, R., & Su, C. (2020). GADM: Manual fake review detection for O2O commercial platforms. Computers & Security, 88, 101657.
Liu, W., He, J., Han, S., Cai, F., Yang, Z., & Zhu, N. (2019). A method for the detection of fake reviews based on temporal features of reviews and comments. IEEE Engineering Management Review, 47(4), 67-79.
Martens, D., & Maalej, W. (2019). Towards understanding and detecting fake reviews in app stores. Empirical Software Engineering, 24(6), 3316-3355.
Baishya, D., Deka, J. J., Dey, G., & Singh, P. K. (2021). SAFER: sentiment analysis-based fake review detection in e-commerce using deep learning. SN Computer Science, 2, 1-12.
Mala, P. R., & Devi, S. S. (2017). Product response analytics in Facebook. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1265-1269). Madurai.
Shelke, N. M., Thakre, V., & Deshpande, S. (2016). Identification of scope of valence shifters for sentiment analysis of product reviews. In 2016 Sixth International Symposium on Embedded Computing and System Design (ISED) (pp. 265-269). Patna.
Hegde, Y., & Padma, S. K. (2017). Sentiment analysis using random forest ensemble for mobile product reviews in Kannada. In 2017 IEEE 7th International Advance Computing Conference (IACC) (pp. 777-782). Hyderabad.
Fang, Y., Wang, H., Zhao, L., Yu, F., & Wang, C. (2020). Dynamic knowledge graph based fake-review detection. Applied Intelligence, 50, 4281-4295.
Barbado, R., Araque, O., & Iglesias, C. A. (2019). A framework for fake review detection in online consumer electronics retailers. Information Processing & Management, 56(4), 1234-1244.
Elmogy, A. M., Tariq, U., Ammar, M., & Ibrahim, A. (2021). Fake reviews detection using supervised machine learning. International Journal of Advanced Computer Science and Applications, 12(1).
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