# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 4 Text-Based Methods
### Tweet Similarity Based Methods  
Tweet similarity methods used in hashtag recommendation find similar tweets to a query tweet using similarity measures [8; 32; 33]. The tweet similarity method was first developed in hashtag recommendation by [PERSON] et al. [8], who used the TF-IDF approach to represent tweets as one-hot vectors. For a query tweet, the set of similar tweets was retrieved using the TFIDF score, where the TFIDF score was calculated for a tweet by summing up the weights of its terms. Their research found that the hashtag extracted from the most similar tweet is the best candidate for recommendation. [PERSON] et al. [33] further improved their method and found that the choice of the similarity measure affected the recommendation's quality, with the cosine similarity measure outperforming the Dice coefficient, Jaccard coefficient and Levenshtein distance. Other works used the same technique but with different tweet representations and similarity measures. [PERSON] et al. 's work [32] was based on the intuition that _tweets can be treated as points in a high-dimensional space and that the correlation between them can be calculated mathematically_. They transformed tweets into a high-dimensional space and constructed a semantic matrix that weights words based on their correlation. They used the Euclidean distance to measure the similarity between tweets. [PERSON] et al. [36] proposed a variation of the TFIDF that considered the hashtag relevancy with terms based on two maps: term-to-hashtag frequency map (THFM) and its reverse map of a hashtag-to-term frequency (HFM). This method took into consideration only the hashtag and word co-occurrences while ignoring their meanings. More recent work has integrated word and sentence embedding techniques in hashtag recommendation. [PERSON] et al. [39] encoded the tweets using the method proposed in [70] and clustered similar tweets using DBSCAN. For a query tweet, they calculated the distances between a query tweet and the centroids of the tweet clusters and extracted candidate hashtags from the closest cluster and put forward recommendations based on the hashtags' popularity values. With the increase in popularity of BERT, it has been used by [PERSON] et al. [40] in generating the embedding of tweets. While most previous research leverages words and hashtags, one paper leverages the _hyperlinked tweets_ which are tweets containing URLs. [PERSON] et al. [34] recommended hashtags to hyperlinked tweets through utilizing the content of tweets and the content of the web pages using various schemes: similarity of tweets; similarity of the linked web pages; similarity of the link domain; and similarity of the named entity in the linked web pages. They applied TFIDF to encode the tweets and web pages and used cosine similarity to calculate the similarity scores. Hashtags were then aggregated and ranked using RankSVM, a learning-to-rank method. Some other researchers calculate the similarity between a tweet and similar hashtags [35; 37; 38]. [PERSON] et al. [38] computed the semantic similarity between a tweet and existing hashtags using the spreading activation technique from multiple semantic networks such as WordNet, DBpedia and Wikipedia. This method expected the words of the tweets and hashtags to be in a formal English form. EmTaggeR [37] is another hashtag recommendation model that derived every hashtag's embeddings by learning the embeddings of words associated with these hashtags. For a given query tweet, the MOWE was used to encode its embeddings. Then, the cosine similarity was used to measure the similarity between a query tweet and hashtags. The tweets with the most similar hashtags were retrieved, and their hashtags were extracted and ranked based on their embeddings score for recommendation. [PERSON] and [PERSON] et al. [35] learnt the topical word embedding of words in a tweet using Topic Enhanced Word Embedding (TEWE).