# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 4 Text-Based Methods
### Probabilistic Methods  
Naive Bayes is a machine learning technique based on the Bayes Theorem. [PERSON] et al. [42] proposed one of the earliest general hashtag recommendation models using Naive Bayes to calculate the conditional probability of a hashtag given a set of terms in the tweet as: \(\mathrm{P}(h|t_{1},t_{2},\ldots,t_{n})=\mathrm{P}(t_{1},t_{2},\ldots,t_{n}|h )\mathrm{P}(h)/\mathrm{P}(t_{1},t_{2},\ldots,t_{n})=\mathrm{P}(h|t_{1}) \mathrm{P}(h|t_{2})\ldots\mathrm{P}(h|t_{n})\). The result is a value between 0 and 1. The hashtags with the highest probabilities were recommended. However, some terms and hashtags never co-occur, leading to a joint conditional probability of 0 even if some words have a high probability score. Topic modelling methods are statistical methods that infer the topics of documents. The inference of topics is derived from the hidden patterns in the corpus. Inspired by the LDA's popularity and its success in deriving topics from large texts, topic-based hashtag recommendation methods manipulate the standard LDA under various assumptions. [PERSON] et al. [45] assumed that _the topics associated with tweets are sufficient to recommend novel hashtags_. Thus, they proposed a general hashtag recommendation model that determined the topics of a tweet, considering the nouns and adjectives, and chose the top topic-terms for recommendation. Although LDA has proven to be a valuable technique for large corpora [71], [PERSON] et al. [72] and [PERSON] et al. [73] have proven that using the standard LDA is not efficient on short texts such as tweets [45]. The terms in tweets are noisy, and word co-occurrence patterns are very sparse [72]. To enhance the quality of the topics generated using LDA and to overcome the problem of short texts in tweets, different schemes that aggregate tweets have been proposed [74; 75]. Usually, such a scheme is applied at the pre-processing phase before training the model. The aggregation schemes that have been investigated in the literature are:  
* The _all-tweets scheme_[75; 76], which aggregates all tweets in a single document and then uses LDA to extract topics;
* The _author-based scheme_, which aggregates all tweets posted by a single user and treats them as a single document [75; 77];
* The _burst-score wise scheme_[75], which aggregates tweets that contain trending terms;
* The _temporal scheme_[75], which aggregates tweets posted at a specific time;
* The _term scheme_[77], which aggregates tweets that share a word--it is applied to every word in the training set;
* The _conversation scheme_[74], which aggregates tweets based on conversations between users, e.g., replies to a tweet sent by other users (co-authors) or by the primary author. According to [PERSON] et al. [74], the best topic quality was generated when the LDA model was trained using the author-based scheme followed by the hashtag-based scheme and then the conversation scheme. Following these findings, [PERSON] et al. [19] adopted the author-based scheme in their hashtag recommendation model and reported good results. In contrast, [PERSON] et al. [46] tackled the noisiness of words in tweets. They assumed that _every tweet is a single local topic, and that there is a global topic for the corpus_. Theyoptimized Twitter-LDA [78], which generates topics for tweets, and proposed TOMOHA. This supervised topic-based hashtag recommendation model considers hashtags as labels of topics and calculates a hashtag's probability based on its local and global words. They also proposed TOMOHA-follow, which extracts the relationship between topics of a tweet, hashtags and the followees topics, assuming that _users follow the topics of their follower_. TOMOHA and TOMOHA-follow achieved similar performances and outperformed the standard LDA. It is difficult for topic-based hashtag recommendation methods to suggest the correct hashtags because of the so-called _vocabulary gap problem_, which is usually caused by the diversified words in tweets and hashtags.