Abstract
Hashtags are considered important in various real-world applications, including tweet mining, query expansion, and sentiment analysis. Hence, recommending hashtags from tagged tweets has been considered significant by the research community. However, while many hashtag recommendation methods have been developed, finding the features from dictionary and thematic words has not yet been effectively achieved. Therefore, we developed an effective method to perform hashtag recommendations, using the proposed Sine Cosine Political Optimization-based Deep Residual Network (SC-Political ResNet) classifier. The developed SCPO is designed by integrating the Sine Cosine Algorithm (SCA) with the Political Optimizer (PO) algorithm. Employing the parametric features from both, optimization can enable the acquisition of the global best solution, by training the weights of classifier. The hybrid features acquired from the keyword set can effectively find the information of words associated with dictionary, thematic, and more relevant keywords. Extensive experiments are conducted on the Apple Twitter Sentiment and Twitter datasets. Our empirical results demonstrate that the proposed model can significantly outperform state-of-the-art methods in hashtag recommendation tasks.
Highlights
The micro-blogging platform Twitter has become one of the best-known social networks on the internet
We model an effective Hashtag Recommendation system using a Deep Residual Network Classifier, trained by the SCPO algorithm, which is derived by integrating the Sine Cosine Algorithm (SCA) and the Political Optimizer (PO) algorithm; The incorporation of features in the optimization algorithm pushes the solution towards the global optimum rather than local optima; We conduct extensive experiments on different datasets, in which the proposed method is shown to outperform state-of-the-art methods in the Hashtag Recommendation task
Long Short-Term Memory (LSTM)-Recurrent Neural Networks (RNNs) Network [15]: For this, the hashtag recommendation was designed by encoding the tweet vector using the LSTM-RNN technique; Pattern Mining for Hashtag Recommendation (PMHRec) [19]: PMHRec was designed using the top k high-average utility patterns for temporal transformation tweets, from which the hashtag recommendation was devised; Attention-based multimodal neural network model (AMNN) [21]: The AMNN was designed to extract the features from text and images, after which correlations are captured for hashtag recommendation; Deep LSTM [26]: Deep-LSTM was designed to evaluate the daily pan evaporation; Emhash [30]: For this method, the hashtag recommendation was developed using a neural network-based BERT embedding; Community-based hashtag recommendation [20]: For this, the hashtag recommendation was designed using the communities extracted from social Networks
Summary
The micro-blogging platform Twitter has become one of the best-known social networks on the internet. Twitter offers a platform wherein users can create a set of follower connections, in order to share their views, and subscribe to subjects posted by their followers. Twitter is considered among the first social networking sites to use the hashtag concept [1]. Due to the proliferation of micro-blogging services, there is an ongoing expansion of short-text over the Internet. Due to the production of huge micro-posts, there exists a requirement for effectual categorization and data searching. Twitter is one of the highly rated micro-blogging sites that permits users to exploit hashtags to classify day-to-day posts. Some tweets do not comprise tags, obstructing the search quality [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
