## 2 Literature Review
### Machine Learning-Based Emotion Analysis Systems  
[PERSON] et al. [13] proposed an emotion prediction and classification model applying a machine learning algorithm. The text dataset used to train the model was labeled into six emotion categories, which are anger, disgust, fear, sadness, joy, and guilt. [PERSON] et al. [14] proposed a machine learning-based emotion analysis model for emotion classification in Chinese microblog texts. This model only classifies texts into two categories, positive and negative. It has the highest accuracy in emotion classification compared with the other related seven models. From a technical point of view, methods using lexicons and predefined rules, unlike machine learning-based methods, do not use complex computational algorithms and perform simple pattern matching; thus, they have the advantage of being able to analyze large numbers of documents in a short time. However, they also have some disadvantages. One is not being able to analyze new terms that are not in the lexicon because the analysis is performed using only the limited data in the lexicon. Another is not being able to properly process idiomatic phrases and emotive expressions consisting of multiple words distributed in a sentence, whose nuances are ambiguous depending on the wider context. On the other hand, machine learning-based methods learn from a large amount of text data and calculate sentiment scores, and have the advantage of being more accurate than rule-based and lexicon-based methods if a large amount of data can be prepared. However, it is an expensive method, as it requires collecting such large datasets, performing data cleaning, accurate expert label annotation, and post-processing. In addition, when using machine learning, it has been pointed out that the more classification classes there are, the less accurate the classifier (classification algorithm) will become [15]. Furthermore, people often express multiple emotions in natural speech, even in one sentence or a single utterance, or in writing, etc. Therefore, even a classifier that supports multiple classes can output only one final class, making it difficult to analyze crowded and complex emotional states. From the above, it can be seen that lexicon-based and rule-based affect analysis methods are still highly applicable. However, the performance of such methods is highly dependent on the lists of words and rules contained in lexicons, and new words and phrases must be regularly added to the affect lexicon from which the rules are derived to ensure and improve the accuracy. Since ML-Ask was originally developed using [PERSON]'s dictionary as the system's database, we used it as a benchmark to investigate other emotion analysis systems for the Japanese language that also use the contents of this dictionary as their database, and whether the system adds words from other dictionaries to this dictionary. Since the compilation of dictionaries is limited to the authors' dictums and biases, and each author's views and opinions are different, almost all dictionaries, especially emotion dictionaries, use a different categorization of the kinds of emotions. For example, the two available dictionaries of Japanese emotive expressions that we decided to add to ML-Ask categorize emotions into eight major categories [16], two major categories, plus many subcategories [17], respectively. Therefore, if other researchers added the vocabulary of other dictionaries to [PERSON]'s dictionary in their emotion analysis or classification system based on [PERSON]'s dictionaries, we further investigated what approaches other researchers have used to unify the vocabulary of various dictionaries with different emotion categories. [PERSON] et al. [18] developed a high-speed emotion analysis system by creating an emotion expression dictionary containing 6660 headwords associated with binary values representing emotions in 10 categories referring to [PERSON]'s dictionary.