This research proposes a novel approach to tackle the challenge of categorizing unstructured customer complaints in the automotive industry. The goal is to identify potential vehicle defects based on the findings of our algorithm, which can assist automakers in mitigating significant losses and reputational damage caused by mass claims. To achieve this goal, our model uses the Word2Vec method to analyze large volumes of unstructured customer complaint data from the National Highway Traffic Safety Administration (NHTSA). By developing a score dictionary for eight pre-selected criteria, our algorithm can efficiently categorize complaints and detect potential vehicle defects. By calculating the score of each complaint, our algorithm can identify patterns and correlations that can indicate potential defects in the vehicle. One of the key benefits of this approach is its ability to handle a large volume of unstructured data, which can be challenging for traditional methods. By using machine learning techniques, we can extract meaningful insights from customer complaints, which can help automakers prioritize and address potential defects before they become widespread issues. In conclusion, this research provides a promising approach to categorize unstructured customer complaints in the automotive industry and identify potential vehicle defects. By leveraging the power of machine learning, we can help automakers improve the quality of their products and enhance customer satisfaction. Further studies can build upon this approach to explore other potential applications and expand its scope to other industries.

People write reviews of numerous products or services on the Internet, in their blogs or community bulletin boards. These unstructured data contain important emotions and opinions about the author's product or service, which can provide important information for future product design or marketing. However, this text-based information cannot be evaluated quantitatively, and thus they are difficult to apply to mathematical models or optimization problems for product design and improvement. Therefore, this study proposes a method to quantitatively extract user’s opinion or preference about a specific product or service by utilizing a lot of text-based information existing on the Internet or online. The extracted unstructured text information is decomposed into basic unit words, and positive rate is evaluated by using existing emotional dictionaries and additional lists proposed in this study. This can be a way to effectively utilize unstructured text data, which is being generated and stored in vast quantities, in product or service design. Finally, to verify the effectiveness of the proposed method, a case study was conducted using movie review data retrieved from a portal website. By comparing the positive rates calculated by the proposed framework with user ratings for movies, a guideline on text mining based evaluation of unstructured data is provided.

Purpose - Civil affairs are increasing in various forms, but civil servants who are able to handle them want to reduce the complaints and provide keywords that will help in the future due to their lack of time. While various ideas are presented and implemented as policies in solving civil affairs, there are many cases that are not policies that people can sympathize with. Therefore, it is necessary to analyze the complaints accurately and to present correct solutions to the analyzed civil complaint data. Research design, data, and methodology - We analyzed the complaints data for the last three years and found out how to solve the problems of Yongin City and alleviate the burdens of civil servants. To do this, the Hadoop platform and Design Thinking process were reviewed, and proposed a new process to fuse it. The big data analysis stage focuses on civil complaints - Civil data extraction - Civil data analysis - Categorization of the year by keywords analyzing them and the needs of citizens were identified. In the forecast analysis for deriving insights, - The case of innovation case study - Idea derivation - Idea evaluation - Prototyping - Case analysis stage used. Results - Through this, a creative idea of providing free transportation cards to solve the major issues of construction, apartment, installation, and vehicle problems was discovered. There is a specific problem of how to provide these services to certain areas, but there is a pressing need for a policy that can contribute as much as it can to the citizens who are suffering from various problems at this moment. Conclusions - In the past, there were many cases in which free traffic cards were issued mainly to the elderly or disabled. In other countries, foreign residents of other area visit the areas for accommodation, and may give out free transportation cards as well. In this case, the local government will be able to set up a framework to present with a win-win scenario in various ways. It is necessary to reorganize the process in future studies so that the actual solution will be adopted, reduce civil complaints, help establish policies in the future, and be applied in other cities as well.