Opinion Mining Based Fake News Detection in Tweeter Sentiment Analysis Using Lexical Cross Mutation Deep Vectorized Convolution Neural Network
Keywords:
Tweet Data Prediction, Fake Content Analysis, Feature Selection and Classification, big data, CNN, opinion miningAbstract
Nowadays, there is a lot of data to process on social media, making it impossible for traditional companies to manage this big data. "Big data" refers to various factual bursts produced by multiple sources. It is categorized into five features or attributes: high volume, different reliability, character and speed. Because this kind of data is beyond the control of conventional systems, this kind of data is beyond relation data, so we can get insights into both structured and unstructured data that had a problematic prediction. To resolve this problem, we propose an Opinion mining-based Fake news detection in tweeter sentiment analysis using Lexical Cross Mutation Deep Vectored Convolution Neural Network (LCM-DVCNN). This process the tweet terms from topic discussion under the dictionary of terms extraction based on Senti Lexicon Demp Score (SLDS), which observes the critical terms to prediction. Then, a unigram evaluation was performed to show the features using the Lexical Uni-Negation Algorithm (LUnA). Based on the feature lexical weights, Topic Cross Mutation Fuzzy Future Selection (TCMFFS) observed the mutual relation features. Then, using the selected features using a Deep Vectorized Conventional Neural Network (DVCNN), these can be predicted based on the gate weights for classifying polar weights. This makes it a better recall in classification accuracy compared to other methods.
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