Extrinsic Evaluation of Distributed Sentence Representation Through Recurrent Neural Networks
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Date
2022
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UMT, Lahore
Abstract
There is an enormous amount of textual data on the internet because of the rise of social media and e-commerce. Consequently, the need for an intelligent model to evaluate and extract relevant information is significant. It is necessary to classify a series of texts into one or more specified categories to use NLP applications like sentiment analysis, web search, spam filtering, and information retrieval. The vanishing gradient problem makes learning long-term dependencies with gradient descent in neural network language models difficult. New strategies have been devised to overcome the limitations of current methods. As the number of parameters in the network grows, so does the computational cost, making it increasingly vulnerable to overfitting. As a result, Natural language processing (NLP) systems treat sentences as discrete atomic symbols, allowing the model to use modest amounts of information about the relationships between the made significant. IMDB reviews are being used in this study to test several deep learning algorithms to identify reviewers' opinions effectively. (NLP) Natural language processing and text analytics have a lot in common with the sentiment. It may be used to assess the reviewer's viewpoint toward various issues or the Review's overall polarity.