Abstract
This paper focuses on detection and classification of spam reviews in a data set of reviews scraped from Twitter. Detection of spam reviews is a major step in screening and blocking irrelevant information posing as a review and can further be used to blacklist users. It is not possible to conclusively classify anything as spam even by human beings. Expecting a machine to do so requires extensive training and exploration of different kinds of models that may offer a multi-dimensional look at the problem that is superior to the discerning ability of human beings. Models should be devised in such a way that they look beyond simple sentiment analysis. In this work, Random Forests and Support Vector machines are designed to work together as a hybrid model to classify reviews. With hybrid methods that utilize ensemble learning, it will be possible to exploit the advantages of multiple models and combine the results to classify a review as spam.
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Acknowledgements
The members of the team would like to express gratitude to professor Ashwini M Joshi, who provided idea and feedback. This work was done while the authors were with PES University.
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Viswanath, H., Singh, R., Gupta, V. (2022). Twitter Spam Review Detection Using Hybrid Machine Learning Techniques. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2020. Lecture Notes in Electrical Engineering, vol 783. Springer, Singapore. https://doi.org/10.1007/978-981-16-3690-5_29
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DOI: https://doi.org/10.1007/978-981-16-3690-5_29
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