Hashtag recommendation for short social media texts using word-embeddings and external knowledge

Hashtag recommendation suggests hashtags to users while they write microblogs in social media platforms. Although researchers have investigated various methods and factors that affect the performance of hashtag recommendations in Twitter and Sina Weibo, a systematic review of these methods is lacking. The objectives of this study are to present a comprehensive overview of research on hashtag recommendation for tweets and present insights from previous research papers. In this paper, we search for articles related to our research between 2010 and 2020 from CiteSeer, IEEE Xplore, Springer and ACM digital libraries. From the 61 articles included in this study, we notice that most of the research papers were focused on the textual content of tweets instead of other data. Furthermore, collaborative filtering methods are seldom used solely in hashtag recommendation. Taking this perspective, we present a taxonomy of hashtag recommendation based on the research methodologies that have been used. We provide a critical review of each of the classes in the taxonomy. We also discuss the challenges remaining in the field and outline future research directions in this area of study.

show abstract

“…Kumar et al [69] incorporated external knowledge from Wikipedia and news articles extracted from the web. Feng and Wang [13] proposed Hybrid+.…”

Section: Hybrid Miscellaneous Methodsmentioning

confidence: 99%

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

2021

View full text Add to dashboard Cite

show abstract

“…Kumar et al. ( 2021 ) used semantic features (based on word-embeddings) and user influence features (based on users’ influential position) to recommend hashtags. Liang et al.…”

Section: Related Workmentioning

confidence: 99%

Hashtag recommendation for enhancing the popularity of social media posts

Chakrabarti

Malvi

Bansal

et al. 2023

Soc. Netw. Anal. Min.

Self Cite

View full text Add to dashboard Cite

Social media has gained huge importance in our lives wherein there is an enormous demand of getting high social popularity. With the emergence of many social media platforms and an overload of information, attaining high popularity requires efficient usage of hashtags, which can increase the reachability of a post. However, with little awareness about using appropriate hashtags, it becomes the need of the hour to build an efficient system to recommend relevant hashtags which in turn can enhance the social popularity of a post. In this paper, we thus propose a novel method hashTag RecommendAtion for eNhancing Social popularITy to recommend context-relevant hashtags that enhance popularity. Our proposed method utilizes the trending nature of hashtags by using post keywords along with the popularity of users and posts. With the prevalent evaluation techniques of this field being quite unreliable and non-uniform, we have devised a novel evaluation algorithm that is more robust and reliable. The experimental results show that our proposed method significantly outperforms the current state-of-the-art methods.

show abstract

“…Two types of approaches have been proposed for hashtag recommendation; solutions that explore the correlation between tweets collection. These solutions use pattern mining for hashtag recommendation [3,13,22]. Other solutions explore the deep learning architectures to learn the diferent patterns, and behaviors from the collection of tweets, and then use inference to recommend the hashtags of the new tweet [7,24,35,44,46].…”

Section: Motivationsmentioning

confidence: 99%

“…Hashtags are metadata added to the main content to easily identify the particular message with a speciic theme or content [4]. Hashtag analysis is at the heart of several complex applications including query expansion [22], sentiment analysis [3], and/or smart cities [32]. Therefore, hashtags recommendation plays an important role in hashtags analysis.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Djenouri

Belhadi

Srivastava

et al. 2023

ACM Trans. Multimedia Comput. Commun. Appl.

View full text Add to dashboard Cite

This paper proposes the use of deep learning in human-computer interaction and presents a new explainable hybrid framework for recommending relevant hashtags on a set of orpheline tweets orpheline tweet: It is a tweet with hashtags. The approach starts by determining the set of batches used in the convolution neural network based on frequent pattern mining solutions. The convolutional neural network is then applied to the set of batches of tweets to learn the hashtags of the tweets. An optimization strategy has been proposed to accurately perform the learning process by reducing the number of frequent patterns. Moreover, eXplainable AI (XAI) is introduced for hashtag recommendations by analyzing the user preferences and understanding the different weights of the deep learning model used in the learning process. This is performed by learning the hyper-parameters of the deep architecture using the genetic algorithm. GPU computing is also investigated to achieve high speed and enable the execution of the overall framework in real time. Extensive experimental analysis has been performed to show that our methodology is useful on different collections of tweets. The experimental results clearly show the efficiency of our proposed approach compared to baseline approaches in terms of both runtime and accuracy. Thus, the proposed solution achieves an accuracy of \(90\% \) when analyzing complex Wikipedia data while the other algorithms did not achieve \(85\% \) when processing the same amount of data.

show abstract

Hashtag recommendation for short social media texts using word-embeddings and external knowledge

Cited by 22 publications

References 34 publications

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review

Hashtag recommendation for enhancing the popularity of social media posts

An Efficient and Accurate GPU-based Deep Learning Model for Multimedia Recommendation

Contact Info

Product

Resources

About