# Hashtag Recommendation Methods for Twitter and Sina Weibo: A Review
## 4 Text-Based Methods
### Classification Based Methods  
Classification is a supervised learning method that trains a classifier to predict a class label given an instance. While binary classifiers tackle only two categories, multi-class classifiers tackle multiple categories. Hashtag recommendation is commonly tackled as a multi-class classification problem of hashtags [49; 52; 53; 56], where every hashtag is considered as a distinct class label. The intuition of the classification based hashtag recommendation is that _the abundance in posts and hashtags equip classifiers with an immense amount of labelled data to learn strong representations_[80]. Classification-based hashtag recommendation requires less _task-specific assumption and engineering_ in comparison with topic-based hashtag recommendation [80].  
Earlier research has applied neural networks and deep learning techniques to predict hashtags. Most of the studies in this area focus only on the internal structure of the words in the tweets to automatically learn their representations and then measure their relevance to hashtags. [PERSON] et al. [49] learned the word distributions using the pre-trained skip-gram model word2 vec on the Google News data set. The mean of the 300-dimensional feature vectors for all the words in a tweet was used to encode the tweet feature vector, which was used as an input to train deep feedforward neural networks. The Mean Squared Error (MSE) was used as an objective function to generate the corresponding hashtag feature vectors. [PERSON] and [PERSON] et al. [53] proposed the topical attention-based LSTM model (TAB-LSTM). They generated local representations of tweets using LSTM and combined them with global representations obtained from the LDA model through an attention mechanism. A softmax classifier was attached at the top of the model to predict the hashtags. [PERSON] et al. [52] proposed a hashtag recommendation method using a Long-Short Term Memory Recurrent Neural Network (LSTM-RNN). The tweets feature vectors were represented using CNN on words pre-trained using Word2 Vec skip-gram model. The generated tweets' feature vectors were then fed into their LSTM-RNN to classify hashtags. The MSE was used as a cost function between the target and the predicted hashtags. Although this method achieved the highest performance over the other techniques mentioned earlier for the top-10 hashtag recommendations, the number of hashtags was limited to 20, which restricted the applicability of the method. Despite all previous methods, [PERSON] et al. [56] proposed the Adaptive neural MEMO Network (AMEN), which models the past tweet history of the user. AMEN encoded tweets using a convolutional layer and hashtags using a recurrent neural network.  
A classification based hashtag recommendation can be an impractical choice of methodology. Hashtags are noisy labels for legitimate classification; i.e., a misspelled hashtag is considered a different hashtag. Hashtag distribution follows the long tail distribution, where most hashtags are low in popularity, and very few hashtags are high in popularity. By using every hashtag as a label, the classifier is more biased toward the more popular hashtags. Therefore, the result of the classification will be inaccurate. In all classification-based hashtag recommendation mentioned earlier, the number of hashtag labels is restricted, which contradicts the nature of hashtag generation in social media. In addition, none of these classifiers was personalized.