The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.
📌 3 Key Takeaways
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Objective & Hypothesis:Representation of words in mathematical expressions is an essential issue in natural language processing.
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Key Findings & Data:Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed.
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Conclusion & Impact:The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:sentiment classification · word embedding · word weight
Abstract
Representation of words in mathematical expressions is an essential issue in natural language processing. In this study, data sets in different categories are classified as positive or negative according to their content. Using the Glove (Global Vector for Word Representation) method, which is one of the word embedding methods, the effect of the vector set based on the word similarities previously calculated on the classification performance has been analyzed. In this study, the effect of pretrained, embedded and deterministic word embedding classification performance has analyzed by using Long Short Term Memory (LSTM). The porposed LSTM based deep learning model has been tested on three different data sets and the results was evaluated.
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