Hashtag Recommendation Research Articles

A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag Recommendation

A Semi-supervised Approach of Cluster-Based Topic Modeling for Effective Tweet Hashtag Recommendation

Improved Hashtag Recommendation Algorithm Determining Appropriate Hashtags for Words with Different Meanings

Improved Hashtag Recommendation Algorithm Determining Appropriate Hashtags for Words with Different Meanings

A semi-supervised approach of short text topic modeling using embedded fuzzy clustering for Twitter hashtag recommendation

Social media stands as a crucial information source across various real-world challenges. Platforms like Twitter, extensively used by news outlets for real-time updates, categorize news via hashtags. These hashtags act as pivotal meta-information for linking tweets to underlying themes, yet many tweets lack them, posing challenges in topic searches. Our contribution addresses this by introducing a novel heuristic for hashtag recommendation. Extracting 20 thousand tweets, 5000 each from distinct categories health, sports, politics, and technology we applied fundamental data cleaning and tokenization techniques. Leveraging Word2Vec, we vectorized tokens, capturing nuanced semantic meanings and mitigating data sparsity issues. The proposed heuristic creates clusters of different topic by combining these embedded features and idea of fuzzy C-Means technique. Develop a rule-based approach that combines both supervised and unsupervised methods to label clusters, indicating their respective topic. The experimental outcomes shows that our proposed techniques achieve better performance metrics in precision, recall, and F1-score compared to specific baseline models.

NLP-enabled Recommendation of Hashtags for Covid based Tweets using Hybrid BERT-LSTM Model

Hashtags have become a new trend to summarize the feelings, sentiments, emotions, swinging moods, food tastes and much more. It also represents various entities like places, families and friends. It is a way to search and categorize various stuff on social media sites. With the increase in the hashtagging, there is a need to automate it, leading to the term “Hashtag Recommendation”. Also, there are plenty of posts on social media sites which remain untagged. These untagged posts get filtered out while searching and categorizing the data using a label. Such posts do not make any contribution to any helpful insight and remain abandoned. But, if the user of such posts is recommended by labels according to his post, then he may choose one or more of them, thus making the posts labelled. For such cases Hashtag recommendation comes into the picture. Although much research work has been done on Hashtag Recommendation using traditional Deep Learning approaches, not much work has been done using NLP based Bert Embedding. In this paper, we have proposed a model, BELHASH, Bert Embedding based LSTM for Hashtag Recommendation. This task is considered as a Multilabel Classification task as the hashtags are one-hot encoded into multiple binary vectors of zeros and ones using MultiLabelBinarizer. This model has been evaluated on Covid 19 tweets. We have achieved 0.72 accuracy, 0.7 Precision, 0.66 Recall and 0.67 F1-Score. This is the first paper of hashtag recommendation to the best of our knowledge combining Bert embedding with LSTM model and achieving the state of the arts results.

WASM: A Dataset for Hashtag Recommendation for Arabic Tweets

WASM: A Dataset for Hashtag Recommendation for Arabic Tweets

Hybrid image analysis model for hashtag recommendation through the use of deep learning methods

Content tags of a given image are the basic mechanism used in social networking services. One of the most commonly used websites like Instagram, Facebook, or Twitter is the hashtag. In this paper, we propose a hybrid solution for automatic image description extraction. A network architecture consists of a module for removing main objects from the image for additional classification of other elements on the background via U-NET (neural model designed for segmentation). Obtained images are classified by multi-branch convolutional networks. The results of such a classification are used to describe the probability of belonging to particular classes. Then, the probability of belonging to each class is used to find related linguistic values through the Skip-Gram module. The main advantage of the solution is the possibility of extending the network with additional branches to increase the number of main classes of objects. The proposal was analyzed through the object’s classification, the use of learning transfer, and image description on two datasets, i.e. Animals-10 and HARRISON16. Experimental results show that the proposal is a very flexible solution for extending the model to other classes without the need to overtrain the entire architecture and reach higher classification accuracy than other models for social networking services.

A Hybrid Deep Neural Network for Multimodal Personalized Hashtag Recommendation

A Hybrid Deep Neural Network for Multimodal Personalized Hashtag Recommendation

Cross-modality representation learning from transformer for hashtag prediction

Hashtags are the keywords that describe the theme of social media content and have become very popular in influence marketing and trending topics. In recent years, hashtag prediction has become a hot topic in AI research to help users with automatic hashtag recommendations by capturing the theme of the post. Most of the previous work mainly focused only on textual information, but many microblog posts contain not only text but also the corresponding images. This work explores both image-text features of the microblog post. Inspired by the self-attention mechanism of the transformer in natural language processing, the visual-linguistics pre-train model with transfer learning also outperforms many downstream tasks that require image and text inputs. However, most of the existing models for multimodal hashtag recommendation are based on the traditional co-attention mechanism. This paper investigates the cross-modality transformer LXMERT for multimodal hashtag prediction for developing LXMERT4Hashtag, a cross-modality representation learning transformer model for hashtag prediction. It is a large-scale transformer model that consists of three encoders: a language encoder, an object encoder, and a cross-modality encoder. We evaluate the presented approach on dataset InstaNY100K. Experimental results show that our model is competitive and achieves impressive results, including precision of 50.5% vs 46.12%, recall of 44.02% vs 38.93%, and F1-score of 47.04% vs 42.22% compared to the existing state-of-the-art baseline model.

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

This article 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, which are tweets 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 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.

Multilingual personalized hashtag recommendation for low resource Indic languages using graph-based deep neural network

Users from different cultures and backgrounds often feel comfortable expressing their thoughts on trending topics by generating content in their regional languages. Recently, there has been an explosion in multilingual information, and a massive amount of multilingual textual data is added daily on the Internet. Using hashtags for multilingual low-resource content can be an effective way to overcome language barriers because it allows content to be discovered by a wider audience and makes it easier for people interested in the topic to find relevant content, regardless of the language in which it was written. To account for linguistic diversity and universal access to information, hashtag recommendation for multilingual low-resource content is essential. Several approaches have been put forth to recommend content-based and personalized hashtags for multimodal content in high-resource languages. Data availability and linguistic differences often limit the development of hashtag recommendation methods for low-resource Indic languages. Hashtag recommendation for tweets disseminated in low-resource Indic languages has seldom been addressed. Moreover, personalization and language usage aspects to recommend hashtags for tweets posted in low-resource Indic languages have yet to be explored. In view of the foregoing, we propose an automated hashtag recommendation system for tweets posted in low-resource Indic languages dubbed as TAGALOG, capable of recommending personalized and language-specific hashtags. We employ user-guided and language-guided attention mechanisms to distill indicative features from low-resource tweets according to the user’s topical and linguistic preferences. We propose a graph-based neural network to mine users’ posting behavior by connecting historical tweets of a particular user and language relatedness by linking tweets according to language families, i.e., Indo-Aryan and Dravidian. Experimental results on the curated dataset from Twitter demonstrate that the proposed model outperformed recognized pre-trained language models and extant research, showing an average improvement of 12.3% and 12.8% in the F1-score, respectively. TAGALOG recommends hashtags that align with the user’s interests and linguistic predilections, leading to a heightened level of tailored and engaging user experience. Personalized and multilingual hashtag recommendation systems for low-resource Indic languages can help to improve the discoverability and relevance of content in these languages.

Unsupervised Keyword Extraction for Hashtag Recommendation in Social Media

Hashtag recommendation aims to suggest hashtags to users to annotate and describe the key information in the text, or categorize their posts. In recent years, several hashtag recommendation methods are proposed and developed to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This paper investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation. To do so, well-known unsupervised keyword extraction methods are applied to three real-world datasets including a new dataset containing texts of user-generated posts on a social learning platform. Experimental evaluations demonstrate that statistical methods performs newer methods including graph-based and embedding-based approaches in generating hashtags for long text, whereas the embedding-based approaches works better on generating hashtags for short texts. As a consequence, it can be concluded that unsupervised keyword extraction models can be adapted for hashtag recommendation when the social platform is new or there is no existing data to develop dedicated supervised learning models.

Hashtag recommendation for enhancing the popularity of social media posts.

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.

DemoHash: Hashtag recommendation based on user demographic information

Social network services have become widely used, and hashtags, which are implicitly involved in delivering specific information, have shown to greatly improve user engagement. A number of prior studies have attempted to recommend appropriate hashtags for each social media user considering his/her posts by consequently extracting the important features from text and images. To develop this multi-dimensionality with hashtag recommendation, user demographic information also plays a significant role in the manner of personalized hashtag recommendation. Thus, this paper proposes the demographic hashtag recommendation (DemoHash) model to utilize users’ demographic information extracted from their selfie images, in addition to textual and visual information. The experimental results with the datasets from Instagram show that our proposed model achieves a greater performance with F1-score, Precision, and Recall than the existing hashtag recommendation methods by average of 4.19%, 18.45%, and 3.91%, respectively. Our approach effectively combined the content-based as well as user-oriented modeling for personalized hashtag recommendation.

Deep learning based hashtag recommendation system for multimedia data

This work aims to provide a novel hybrid architecture to suggest appropriate hashtags to a collection of orpheline tweets. The methodology starts with defining the collection of batches used in the convolutional neural network. This methodology is based on frequent pattern extraction methods. The hashtags of the tweets are then learned using the convolution neural network that was applied to the collection of batches of tweets. In addition, a pruning approach should ensure that the learning process proceeds properly by reducing the number of common patterns. Besides, the evolutionary algorithm is involved to extract the optimal parameters of the deep learning model used in the learning process. This is achieved by using a genetic algorithm that learns the hyper-parameters of the deep architecture. The effectiveness of our methodology has been demonstrated in a series of detailed experiments on a set of Twitter archives. From the results of the experiments, it is clear that the proposed method is superior to the baseline methods in terms of efficiency.

Task-agnostic representation learning of multimodal twitter data for downstream applications

Twitter is a frequent target for machine learning research and applications. Many problems, such as sentiment analysis, image tagging, and location prediction have been studied on Twitter data. Much of the prior work that addresses these problems within the context of Twitter focuses on a subset of the types of data available, e.g. only text, or text and image. However, a tweet can have several additional components, such as the location and the author, that can also provide useful information for machine learning tasks. In this work, we explore the problem of jointly modeling several tweet components in a common embedding space via task-agnostic representation learning, which can then be used to tackle various machine learning applications. To address this problem, we propose a deep neural network framework that combines text, image, and graph representations to learn joint embeddings for 5 tweet components: body, hashtags, images, user, and location. In our experiments, we use a large dataset of tweets to learn a joint embedding model and use it in multiple tasks to evaluate its performance vs. state-of-the-art baselines specific to each task. Our results show that our proposed generic method has similar or superior performance to specialized application-specific approaches, including accuracy of 52.43% vs. 48.88% for location prediction and recall of up to 15.93% vs. 12.12% for hashtag recommendation.

Knowledge Discovery from Large Amounts of Social Media Data

In recent years, social media analysis is arousing great interest in various scientific fields, such as sociology, political science, linguistics, and computer science. Large amounts of data gathered from social media are widely analyzed for extracting useful information concerning people’s behaviors and interactions. In particular, they can be exploited to analyze the collective sentiment of people, understand the behavior of user groups during global events, monitor public opinion close to important events, identify the main topics in a public discussion, or detect the most frequent routes followed by social media users. As an example of the countless works in the state-of-the-art on social media analysis, this paper presents three significant applications in the field of opinion and pattern mining from social media data: (i) an automatic application for discovering user mobility patterns, (ii) a novel application for estimating the political polarization of public opinion, and (iii) an application for discovering interesting social media discussion topics through a hashtag recommendation system. Such applications clearly highlight the abundance and wealth of useful information in many application contexts of human life that can be extracted from social media posts.

PAC-MAN: Multi-Relation Network in Social Community for Personalized Hashtag Recommendation

Previous personalized hashtag recommendations have been able to recommend suitable hashtags for a given microblog. Despite their performance improvement, we argue that three challenges remain unexplored. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , prior studies capture user interests solely from user-hashtag interactions that are directly connected (i.e., first-order relations), making them unable to deal with multiple user behaviors, including user-user social and hashtag-hashtag co-occurrence, and also restrict relations from similar users that are indirectly connected (i.e., high-order relations). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , previous works personalize content at the microblog level, ignoring the personalized aspects that users have for each word in the microblog. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Third</i> , past studies capture correlations among hashtags in the same microblog from only the left-side correlations, restricting the right-side correlations. In this paper, we propose a novel integral model for personalized hashtag recommendation named PAC-MAN, which explores high-order multiple relations to model fruitful user and hashtag representation before fusing with word representation for word-level personalization and integrating with sequenceless hashtag correlation for the recommendation. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">First</i> , to derive fruitful user and hashtag representation from higher-order multiple relations, Multi-relational Attentive Network (MAN) applies GNN to jointly capture relations on three communities: (1) user-hashtag interaction; (2) user-user social; and (3) hashtag-hashtag co-occurrence. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Second</i> , to personalize content at a word level, Person-And-Content based BERT (PAC) extends BERT to input not only word representations from the microblog but also the fruitful user representation from MAN, allowing each word to be fused with user aspects. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Finally</i> , to capture sequenceless hashtag correlations, the fruitful hashtag representations from MAN that contain the hashtag’s community perspectives are inserted into BERT to integrate with the hashtag’s word-semantic perspectives, and a hashtag prediction task is then conducted under the mask concept, enabling hashtag correlations to be obtained from both left and right sides without sequence constraints. Extensive experiments on the Twitter dataset demonstrate that PAC-MAN consistently outperforms state-of-the-art methods, including neural network based and traditional graph based methods, over precision, recall, and F1-score metrics.

Hashtag Recommendation System for Instagram Posts using Transfer Learning with EfficientNet and ALS Model

Hashtag Recommendation System for Instagram Posts using Transfer Learning with EfficientNet and ALS Model

SC-Political ResNet: Hashtag Recommendation from Tweets Using Hybrid Optimization-Based Deep Residual Network

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.

Learning Sentence-to-Hashtags Semantic Mapping for Hashtag Recommendation on Microblogs

The growing use of microblogging platforms is generating a huge amount of posts that need effective methods to be classified and searched. In Twitter and other social media platforms, hashtags are exploited by users to facilitate the search, categorization, and spread of posts. Choosing the appropriate hashtags for a post is not always easy for users, and therefore posts are often published without hashtags or with hashtags not well defined. To deal with this issue, we propose a new model, called HASHET ( HAshtag recommendation using Sentence-to-Hashtag Embedding Translation ), aimed at suggesting a relevant set of hashtags for a given post. HASHET is based on two independent latent spaces for embedding the text of a post and the hashtags it contains. A mapping process based on a multi-layer perceptron is then used for learning a translation from the semantic features of the text to the latent representation of its hashtags. We evaluated the effectiveness of two language representation models for sentence embedding and tested different search strategies for semantic expansion, finding out that the combined use of BERT ( Bidirectional Encoder Representation from Transformer ) and a global expansion strategy leads to the best recommendation results. HASHET has been evaluated on two real-world case studies related to the 2016 United States presidential election and COVID-19 pandemic. The results reveal the effectiveness of HASHET in predicting one or more correct hashtags, with an average F -score up to 0.82 and a recommendation hit-rate up to 0.92. Our approach has been compared to the most relevant techniques used in the literature ( generative models , unsupervised models, and attention-based supervised models ) by achieving up to 15% improvement in F -score for the hashtag recommendation task and 9% for the topic discovery task.