Operating Machine Learning across Natural Language Processing Techniques for Improvement of Fabricated News Model (original) (raw)
Fake news or fabricated news, refers to false information published under the guise of being authentic news, often to influence political views. Fabricated news articles are a threat to people's trust in the government and in effect, one of the biggest threats that modern-day democracies are facing. As the menace of fake news is growing with each passing day, so is the research community getting more actively involved in curbing this issue. This paper reviews the current progress of the advancements done to solve the issue. The paper also presents various ensemble techniques to perform the binary classification of news articles. Additionally, Natural Language Processing (NLP) emerges as one of the hottest topic in field of speech and language technology and Machine Learning (ML) can comprehend how to perform important NLP tasks. This is often achievable and cost-effective where manual programming is not. This paper strives to study NLP and ML and gives insights into the essential characteristics of both. It summarizes common NLP tasks in this comprehensive field, then provides a brief description of common machine learning approaches that are being used for different NLP tasks. Also this paper presents a review on various approaches to NLP and some related topics to NLP and ML. Respectively and with regard to this research article, fake news detection research is still in the early stage as this is a relatively new phenomenon in the interest raised by society. Machine learning helps to solve complex problems and to build AI systems nowadays and especially in those cases where we have tacit knowledge or the knowledge that is not known. We used machine learning algorithms and for identification of fake news; we applied three classifiers; Passive Aggressive, Naïve Bayes, and Support Vector Machine. Simple classification is not completely correct in fake news detection because classification methods are not specialized for fake news. With the integration of machine learning and text-based processing, we can detect fake news and build classifiers that can classify the news data. Text classification mainly focuses on extracting various features of text and after that incorporating those features into classification. The big challenge in this area is the lack of an efficient way to differentiate between fake and non-fake due to the unavailability of corpora. We applied three different machine learning classifiers on two publicly available datasets. Experimental analysis based on the existing dataset indicates a very encouraging and improved performance.
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