# Stepwise Multi-Task Learning Model for Holder Extraction in Aspect-Based Sentiment Analysis
## 1 Introduction  
For a coarse sentiment analysis at the sentence- or document-level, the entire content of the sentence or the document is classified as one sentiment polarity and sentiment expressions play an important role in finding the correct polarity in most methods. On the other hand, for a fine-grained sentiment analysis (SA), aspect-based sentiment analysis (ABSA) is a text analysis technique that categorizes data by aspect and identifies the sentiment attributed to each one [1]. Figure 1 shows an example of ABSA. There is one aspect in the sentence, _\"[PERSON] said that the T-bone steak was delicious\"_, in which the FOOD aspect is positive. As shown in Figure 1, to classify sentiment polarity for aspects, we need three types of information: the sentiment word (\"delicious\"), target (\"T-bone steak\"), and holder (\"[PERSON]\") of the aspect (\"FOOD\"). The sentiment word is an expression containing sentiment about the aspect in the text. The target is an object that the sentiment word modifies within the text. The holder is the person who expresses the sentiment for the aspect by using the sentiment word for the target. In particular, the sentiment polarity of all aspects needs to be identified when multiple aspects are presented in one sentence or document (see Figure 2). There are two aspects in the sentence, _\"The pepperoni pizza was delicious, but the staff was dreadful\"_, in which the FOOD aspect (colored in blue) is positive, while the SERVICE aspect (colored in red) is negative. Such problems are a significantly more challenging task for a fine-grained sentiment. Generally, ABSA tasks can be divided into aspect-entity extraction and aspect-level sentiment classification. However, aspect-level sentiment classification requires aspect-entity extraction. In aspect-entity extraction, there are five sub-level tasks: sentiment word, target, holder, aspect, and entity extraction. These are characteristic of fine-grained SA and are information that is required to analyze sentiment accurately for aspects. However, we tackle sentiment word and target extraction as well as holder extraction but focus on the holder extraction due to the following three reasons. First, the aspect and the entity are fixed in Laptop and Restaurant datasets, disclosed in SemEval-2014 through 2016 [1; 2; 3] which are most frequently used in the ABSA task. Second, extracting the sentiment word, the target, and the holder should be performed at once according to [PERSON] [4] because they are linked to one aspect. They should be extracted in sequence because the sentiment word is an important clue for extracting the target, which is key evidence of the holder. Namely, the three types of information sequentially become an important clue. Therefore, the holder should be extracted in order to obtain all information in ABSA. Third, an aspect-level sentiment classification cannot be performed in non-annotated data excluding the existing datasets of the Laptop and the Restaurant. This task outputs only three classification results--positive, negative, and neutral--using sentiment word, target, and holder information that is provided in the data. Therefore, it can be considered as performing a coarse SA by providing three pieces of information called stepwised features in this paper. We thus design and experiment a system that outputs sentiment word, target, and holder, by taking the raw text in Laptop and Restaurant datasets as input. For ABSA, multiple aspects can be presented in one data. Therefore, sentiment expressions in a text affect the polarity classification of aspects but can also be an important clue for identifying the target modified by the sentiment expressions. [PERSON] and [PERSON] [5] and [PERSON] and [PERSON] [6] conducted experiments on the extraction of sentiment words and collocation nouns that frequently co-occur by identifying whether sentiment words are present within the text using a sentiment lexicon. [PERSON] et al.