Abstract
In the evolution of the Internet, social media platform like Twitter has permitted the public user to share information such as famous current affairs, events, opinions, news, and experiences. Extracting and analyzing keyphrases in Twitter content is an essential and challenging task. Keyphrases can become precise the main contribution of Twitter content as well as it is a vital issue in vast Natural Language Processing (NLP) application. Extracting keyphrases is not only a time-consuming process but also requires much effort. The current works are on graph-based models or machine learning models. The performance of these models relies on feature extraction or statistical measures. In recent year, the application of deep learning algorithms to Twitter data have more insight due to automatic feature extraction can improve the performance of several tasks. This work aims to extract the keyphrase from Big social data using a sentence transformer with Bidirectional Encoder Representation Transformers (BERT) deep learning model. This BERT representation retains semantic and syntactic connectivity between tweets, enhancing performance in every NLP task on large data sets. It can automatically extract the most typical phrases in the Tweets. The proposed Semkey-BERT model shows that BERT with sentence transformer accuracy of 86% is higher than the other existing models.
Highlights
In recent days, extracting keyphrases from online social data has played a critical role
There is a subtle difference between keyword and keyphrase extraction
This paper presents an attention-based deep learning model for contextual key phrases with Bidirectional Encoder Representation from Transformer
Summary
In recent days, extracting keyphrases from online social data has played a critical role. In machine learning approaches namely unsupervised and supervised methods are widely used for keyword extraction. Devika R et al.: A Deep Learning Model for Semantic Keyphrase Extraction on Big Social Data. Many studies on keyphrase extraction are using supervised learning methods like Naïve Bayes, Support Vector Machine (SVM), and unsupervised methods like Term Frequency - Inverse Document Frequency (TF-IDF) has witnessed a good performance [12]. These methods depend on feature extraction efforts [13]. Deep learning-based approaches widely provided significant benefits for the task of keyphrase extracting an NLP task.
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