# Topic Word Embedding-Based Methods for Automatically Extracting Main Aspects from Product Reviews
## 6 Conclusions  
In data-driven market research, given a collection of large review posts about a particular product, it is important to automatically find main aspects that are mainly talked by most customers. For each aspect, the automatic method that quickly and easily grasps the public's preferences for the product and by finding specific reasons they like or dislike will provide useful information to customers who want to purchase the product or company marketers who want to sell the product. In particular, finding main aspects automatically is the essential task in market research. To address this problem, we focus first on utilizing the traditional topic model that extracts topics from large review text documents. In our standpoint, a topic is represented as an aspect. However, one obstacle of the existing topic models is that the topic results are often dirty. In other words, there exist ambiguous topics, redundant topics, and meaningless topics. To improve the dirty topics, we propose an automatic method (Algorithm 1) that estimates priors that fit the input data and a novel topic cleaning method (Algorithm 2) that both split ambiguous topics and merge duplicate topics. Moreover, based on the cleaned topics, we propose novel word-level (Algorithm 3), sentence-level (Algorithm 4), and hybrid (Algorithm 5) methods that automatically label each topic as an aspect. All algorithms are based on word embedding model. In Algorithm 1, various word embedding models are used to measure topic coherence. In Algorithm 2, word embedding models are used to merge redundant topics. In Algorithms 3-5, to find the center word among relevant words in each topic, word embedding models are used. In a collection of review text documents about hotels, the experimental results show that Algorithm 1 using fastText is the best word embedding model with priors \(\alpha=1.5\) and \(\beta=0.08\). The rouge-1 score of Algorithm 1 is 0.725, while that of the baseline method with default priors is 0.582, improving up to 25%. In addition, comparing original topics to split topics, Algorithm 2 improves to about 25% on average. In case of merged topics rather than original topics, the average \(F_{1}\)-score is about 0.91. These results clearly show that the topics cleaned by Algorithm 2 is more useful than the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Topic ID** & **Original Topic Coherence** & **Split Topic Coherence** & **Improvement Rate** \\ \hline  
Average & 0.5 & 0.62 & 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results of original and split topics. original topics in order to label topics. Finally, human evaluators judges how useful the proposed three methods of automatically labelling topics are. In particular, the survey scores of the sentence-level method are about 4.4 on average that is close to 5 as the perfect score. As a case study, main aspects about the hotel data set are breakfast, interior, service, location, and landscing. All of these results indicate the proposed methods provide a simple, fast, and automatic way to mine main aspects from a large review data. To the best of our knowledge, this work is the first study to consider (1) an empirical method that is effective in estimating the priors from ambiguous and noisy text data, (2) a topic clean method that splits and merges ambiguous and redundant topics, and (3) word-level and sentence-level topic labelling methods that are based on various word embedding models. In our future work, we plan to apply the proposed method to various domains including hotels, restaurants, rent-a-car, and so forth, and improve the accuracy of the proposed method. We will also propose a new abstractive summarization method based on clean topic information. Furthermore, we will re-implement the proposed method to MapReduce-based method for processing Big Data. All authors of this article discussed the contents of the manuscript and actively contributed in the processing of implementation. Conceptualization, B.-W.O. ; methodology, B.-W.O. ; software, S.-M.P.