# Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews
## 1 Introduction  
Opinion Summarization is one of the most important problems in sentiment analysis and opinion mining areas currently. Given a large collection of review text documents about a particular product in automobiles or smartphones as input, state-of-the-art opinion summarization methods usually provide (1) a few main aspects, (2) pros and cons per aspect, and (3) extractive or abstractive summary in favor of/against each aspect. In this article, an aspect is formally defined as _a focused topic_ about which a majority of online consumers mainly talk in the particular product. For instance, according to domain experts, _design_, _function_, _performance_, _price_, _quality_, and _service_ are the main aspects in automobiles [1]. Regardless of manual or automatic approaches, if we can precisely mine key aspects from a large corpus, we can easily measure pros and cons ratios per aspect. Nowadays, since data-based market research has become popular, the aspect detection problem has been studied actively. To address this challenging problem of _automatically_ detecting a few main aspects from a large corpus, association rule mining-based, lexicon-based, syntactic-based, and machine learning-based approaches are presented as solutions [2]. However, despite active research on the aspect detection problem, it is still not trivial to find the right aspects for each domain. To make matters worse, major aspects are different for each domain and differ slightly by different consumer groups even on the same domain. In addition, today demand for handling large-scale text data and the existence of lexical semantics (ambiguous words) make this problem even harder. In particular, to surmount thechallenges for which conventional solutions against this problem no longer work, we propose a novel automatic method of mining main aspects through topic analysis. Given a collection of text documents, a probabilistic topic model extracts major latent topics that are mainly spoken in the collection. A topic \(\theta_{i}\) consists of relevant words, where each relevant word \(w\) has its probability value \(P(w)\) that indicates its importance (weight) of \(w\) within \(\theta_{i}\). For example, \(\theta_{i}\) is represented as a set of relevant words like {bank:0.5, saving: 0.35, interest: 0.11, loan: 0.04}. Probabilistic Latent Semantic Analysis (pLSA) or Latent Dirichlet Allocation (LDA), well-known as topic models, extracts topics from large text documents by the number of topics (\(k\)) entered by a domain expert. Then, he/she manually determines the label (title) of each topic \(L(\theta_{1}),\dots,\) and \(L(\theta_{k})\) by looking at the relevant words and taking advantage of domain knowledge. For example, \(L(\theta_{i})\) is very likely to be the word _finance_ as the label if he/she guesses roughly with the topic words like 'bank','saving', 'interest', and 'loan'. In other words, _finance_ includes the comprehensive meaning of all words in \(\theta_{i}\). To mine aspects from large text documents, Figure 1 presents the overall system of the proposed approach, where we first extract accurate topics that are mostly covered in the collection and then generate important aspects through clean topics. To acquire high-quality topics, we propose novel two approaches that improve the shortcomings of the existing topic models. * Firstly, we propose a new algorithm of estimating proper priors for a given topic model. The Dirichlet priors that yield the strong association of all words in a topic (topic coherence) are chosen while the topic model with different prior values is executing iteratively. In particular, we propose the word embedding-based measure to compute the average topic coherence value. We also propose a new method of automatically identifying the number of true topics in advance, where the number of true topics means the number of topics originally in a given data that is unknown.