Clickbait Identification in Social Media Text using LSTM based Approach
DOI:
https://doi.org/10.61841/7179a798Keywords:
Clickbaits, Long Term Short Memory, Social Media Text, VectorizationAbstract
Capsule networks can also be used to investigate the working under the input of text data rather than images. Such limitations were taken into account while identifying clickbaits in the usage of texts from social media. Several systems in existing work focus on the usage of the Long Term Short Memory (LSTM) approach to identify clickbaits. Due to certain disadvantages, the issue was considered a research focus area. The proposed system utilizes a three-layered architecture where the first layer takes the input as text. After vectorization, the input is categorized, and the final layer produces the output. The layered architecture also takes less response time, thereby making the system efficient, which is showcased in the experimental results obtained after implementation.
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