# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 4 Text-Based Methods
### Probabilistic Methods
Thus, machine translation models were adopted in hashtag recommendation mainly to solve this problem. Most of the proposed hashtag recommendation methods based on machine-translation worked under the assumption that _each tweet and the associated hashtags have parallel descriptions but are written in different languages_[2; 79]. [PERSON] et al. [1] introduced the topic-specific translation model (TSTM), which integrated the use of the LDA and a _word alignment technique_. Their paper's main idea was to promote the essential words in each tweet to trigger the corresponding hashtags and assess the topic-specific word alignment probabilities between a tweet and a hashtag. [PERSON] et al. [2] later improved their original model and called it TTM. In their previous model, TSTM, they assumed that there are \(k\) topics. In their optimized model, TTM, they believed that there are \(k+1\) topics, where the additional topic includes the _background words_ that are mostly used in most of the tweets but do not belong to a specific topic. Their optimized model outperformed the original model. Rather than using the LDA, [PERSON] et al. [3] adopted a Dirichlet Process Mixture Model (DPMM) and implanted a hidden variable that represented the hashtag type. Their model DPMM applied the Hierarchical Dirichlet Process (HDP) to automatically capture an infinite number of topics from the data instead of specifying it in advance as in the standard LDA. The hashtag recommendation models mentioned above are general. They ignore user information, preferences, and global trends such as temporal information. TUK-TTM [7] incorporated personal and temporal factors as hidden variables. The personal variable was calculated by computing the probabilities of the topics that the user was interested in. The temporal aspect was calculated by computing the probabilities of the global trend at a certain period. TUK-TTM was trained on the optimized LDA on aggregated tweets where tweets posted in a month were considered a single document. They assigned a hidden variable to denote the topic's probability for a tweet and used the translation model to get the hashtags. They found that topics vary over time. Unlike the above word-based topical translation models, which treat tweets on the word level, [PERSON] et al. [4] proposed a phrase-based topical translation model to perform the hashtag recommendation task. They refer to their method as PTTM (abbreviated for Phrase Topical Translation Model). They assumed that _a phrase is a natural unit that conveys a complete topic and contains real meaning that a human being wishes to express_. Under this assumption, they aligned phrases in a tweet with hashtags. They adopted a phrase extraction technique that retrieves frequent phrases from the corpus and merges the words in a given tweet into phrases. They treated each tweet as a _bag of phrases_ rather than a _bag of words_ as used in most of the LDA based models and assigned a single topic to each tweet. This method outperformed all previously mentioned models with the number of topics ranging from 10 to 30. In contrast, [PERSON] et al. [11] studied the social factor's impact through user mentions under the assumption that _users who mention each other more often are most likely to adopt similar hashtags_. Their PLSA-style model was regularized to study the importance of the social factor. They proposed the Content-Pivoted Model and the Hashtag-Pivoted Model. The former model worked under the assumption that _the user figures out the hashtag while he/she writes a tweet_, whereas the latter assumed that _the user has a pre-selected hashtag in mind_. The Content-Pivoted Model generated the topics of the tweet considering the user topic distribution and the time topic distribution. The Hashtag-Pivoted Model generated the topics of the tweet considering an additional factor, which was the hashtag distribution. It was found that the Hashtag-Pivoted Model outperformed the Content-Pivoted Model. In both models, existing hashtags were recommended.