# Topic-Specific Emotion Mining Model for Online Comments
## 1 Introduction  
As time goes on, consumers are increasingly dependent on online shopping [1]. Simultaneously, the scale of transaction and size of users in the online shopping market have continued to expand. Accordingly, there are plenty of shopping websites, such as Taobao, Jingdong, Amazon, and so on. Almost all e-commerce platforms provide users with the function of online comments to express their views and feelings after receiving their goods. So far, each online shopping platform has accumulated enormous numbers of comments [2]. Online comments that mingle opinions, emotions, aspects, and time stamps contain rich and valuable knowledge [3]. An example of online comment is shown as Figure 1. In general, what's useful to us are the comment content and the timestamp.  
Emotion mining for online comments certainly has been an attractive topic of study [4; 5] and has a wide range of applications, such as product improvement [6] and product recommendation [7]. Through the excavation of commentary emotion, sellers can grasp buyers' attitude on products and services to make better marketing strategies. Consumers can get a reference to pick the most favored products.  
Currently, the methods for sentiment analysis are based on lexicon [8] and machine learning [9]. These methods mainly concentrate on document-level emotion mining. When we need to understandemotion of a specific topic for a product, such as the screen of the phone, it is not enough. To discover a particular topic's emotion of the entity, many researchers extend Latent Dirichlet Allocation (LDA) [10] by incorporating a sentiment layer [11; 12]. Topic models based on LDA are capable of mining effectively the topic-level emotions to a certain extent. Nevertheless, these bag-of-words models do not adaptively determine the number of topics and ignore the correspondence between opinions and aspects. Uncertainty in the number of topics inevitably reduces the accuracy of topic-level sentiment analysis. A single word contains less semantic information and its emotion is ambiguous. Thus, low accuracy of emotion mining and poor interpretability of results can be caused by ignoring interword relationships. Meanwhile, the dimension of the comments that is represented by a bag of words is higher, which leads to time-consuming calculations.  
To overcome the boundedness of the conventional topic models, this paper puts forward a topic model named Topic-specific Emotion Mining Model (TSEM), which not only incorporates relationships between words, but also automatically gets the number of topics through the perplexity [13] indicator. The generation model can be described as follows. Firstly, an aspect-opinion pair's latent topic is chosen from time-specific topic distribution. The time of each review text is known. Then the aspect-opinion pair's sentiment is generated from time-topic emotional distribution under the condition of topic label and time label of the word pair ascertained. Secondly, an aspect-opinion pair is sampled from emotion-specific and topic-specific word pair distribution after getting the emotion and topic of the pair.  
Because users' sentiment is uncomplicated in commodity reviews, it is not necessary to divide emotion into multiple dimensions. Therefore, we only divide the sentiment of online comments into positive and negative emotions.  
The rest of this paper is arranged as below. We show the related work in Section 2. Section 3 introduces our topic model for online comments in details. Section 4 is the experimental setting and the analysis of the experimental results. At last, we make a conclusion in Section 5.