# Topic-Specific Emotion Mining Model for Online Comments
## 2 Related Work
So, identifying both topic and sentiment simultaneously should play a vital role in topic-level emotion mining. With all this in mind, the probabilistic generative model will be a good choice in mining topic-level emotion, which can achieve the goal of detecting topic and emotion at the same time. Therefore, many scholars focus their attention on the topic model to mine the topic-level emotions. Extended models based on topic model are also widely applied to online comment emotion mining, which can effectively analyze online comments' emotions at topic level. [PERSON] [24] built the Topic-Sentiment Mixture (TSM) model, which separated sentiment model from topic model resulting in a lack of connection between emotions and topics. [PERSON] and. [PERSON] [25] introduced a Markov chain to extend LDA and named the model ADM-LDA that was able to extract multiword aspects. However, this model does not take into account the emotion of words. [PERSON] [11] proposed Joint Sentiment/Topic (JST) model to identify topics and emotions from texts simultaneously. [PERSON] [12] firstly proposed Sentence-LDA (SLDA), which assumed that all words in a sentence only belonged to one topic, and then extended SLDA to Aspect and Sentiment Unification Model (ASUM). Joint Aspect-based Sentiment Topic (JAST) model [26] can solve multiple subproblems of aspect-level sentiment analysis by modeling these subproblems under a unified framework. But it only contains limited semantic information. [PERSON] [27] proposed the Specification Latent Dirichlet Allocation (SpecLDA) model to mine comment texts relevant to a feature value, which combined product reviews and specifications that were composed of structured information. However, the acquisition of specifications is not easy. JST and ASUM are the most similar methods to ours. ASUM assumes that all words in a sentence belong to only one topic, which is not in line with the actual situation. In fact, a sentence can also be a mixture of multiple topics. Our model samples a topic for each word instead of each sentence, which ensures that the sampled topic was more accurate. Besides, the most essential difference of our method is the difference of input compared with JST and ASUM. Their input are words of documents in corpora, while our inputs are aspect-opinion pairs of documents. In other words, we see each document as a bag of word pairs to better represent the corpora. The advantages of our method are as follows: firstly, the dimension of each document is greatly reduced, which greatly cuts the computation time of the model; secondly, compared with a single word, aspect-opinion pairs increase semantics to improve the accuracy of emotion classification; thirdly, it can output the associated linked network of each topic, not just word distribution, so as to facilitate human understanding of the results of emotion mining at the topic level. In summary, the previous methods have been able to solve the problem of the emotional analysis for online reviews to a certain extent. But, because of neglecting the dependencies between words, these methods are more suitable for the case where the words in a text are independent of each other. In contrast, our model considers interword relationships and determines the number of topics beforehand to achieve the goal of reducing computation time, improving the accuracy rate of emotion mining at the topic level, and making the results easier to understand.