# Topic-Specific Emotion Mining Model for Online Comments
## 2 Related Work  
In this section, the related work of emotion mining for online comments is firstly reviewed. Then, we will introduce the existing work of the topic-level emotion mining in this field. Traditional online review emotion mining methods focus on machine learning [4; 9; 14], lexicon [8; 15; 16], and mixed methods [17; 18]. [PERSON] [9] applied Naive Bayes, Support Vector Machine (SVM), and the character-based N-gram model to online review emotion mining, and experiments showed that SVM and the N-gram model have superior classification effects. [PERSON] [14] used the classifier combine rule to combine emotional classification results of classifier Support Vector Machine and Hidden Markov Model. [PERSON] [4] employed four different machine learning algorithms to classify emotions, including Naive Bayes, Maximum Entropy, Stochastic Gradient Descent, and Support Vector Machine. [PERSON] [8] developed a lexicon-enhanced method, which used sentiment words based on a sentiment lexicon to set up a new feature dimension. [PERSON] [15] compared the two methods of emotion mining: lexicon-based and supervised learning, and proved that the lexicon-based method had a better performance in emotion  
Figure 1: Online commodity evaluation. classification. [PERSON] [16] presented different lexicon construction techniques and assigned the most frequent polarity label to the corresponding aspect category. [PERSON] [17] proposed two methods: emotion classification based on the number of emotional words and using Support Vector Machines. Then, combining the two methods improved the performance of results. [PERSON] [18] combined rule-based classification and machine learning to construct different emotion classifiers. However, when using machine learning for sentiment classification, most of the existing methods are mainly based on supervised learning models, which are trained from tagged corpora. That is to say, each document in the corpora should be marked with emotional classification (e.g., positive or negative) before being trained. Such tagged corpora are commonly difficult to obtain in practical application. When applying lexicon-based methods to sentiment classification, most existing methods classify the emotions of a text through the emotions of words in the lexicon, which neglects the context of words. Once the emotions of words change with the context, it will inevitably lead to a large deviation between the result of emotional classification and the actual emotion of the text. In addition, when classifying text into the more fine-grained emotions (e.g., emotional classification for the topic \"price\" of a product), the above-mentioned approaches are often necessary to first detect the topic/feature and then categorize the emotion for that specific topic/feature. Besides the above-mentioned methods, there are other methods for topic-level emotion mining. Topic-level emotion mining contains two subtasks: aspect-based topic extraction [19; 20; 21] and topic-based emotion classification [22]. Further attempts are mainly concentrated on completing one of the two subtasks. [PERSON] [19] used a multi-aspect bootstrapping method for aspect identification. [PERSON] [20] proposed a rule-based approach to detect aspects by exploiting common-sense knowledge and sentence dependency trees. [PERSON] [21] proposed an algorithm to compute a score for each implicit aspect and introduced a threshold parameter to filter out aspects whose score is too low. [PERSON] [22] used frequent item set mining to extract aspect and utilized supervised learning algorithms to identify emotion of each aspect. [PERSON] [23] used a rule-based approach to find feature-opinion pairs and determined emotion of opinion based on lexicon. Sentiment classification is closely related to topics or domains. Intuitively, it is feasible to combine the above two subtasks to classify emotions at the topic level, but it is a little troublesome. No matter which subtask is not completed well, it will bring poor performance of the final emotional classification results.