Text mining-based four-step framework for smart speaker product improvement and sales planning

Abstract

The smart speaker market, which is considered an early-stage market, is expected to grow rapidly as smart speakers become a part of daily life. Consequently, manufacturers are trying to dominate the market. To achieve this, they must analyze users' reactions to their products and find insights for product improvement through comparison with competitors. We propose a four-step methodological framework for identifying meaningful opinions from a large number of online user reviews. First, network analysis is conducted to compare differences between brands. Next, through topic modeling, the attributes of speakers that users consider the most crucial are extracted for each brand. Third, sentiment analysis is conducted to examine how users' emotional polarities differ for each attribute. Through this, product improvement and product sales plans can be derived. Finally, in order to clarify the strengths and weaknesses of each brand, brand positioning is conducted and user opinions that have changed along with the evolution of the speaker's generation are analyzed. Our study identified the factors that positively or negatively affect the experience of smart speaker users. In addition, the proposed method is highly useful because it can be used to derive insights from a large amount of user opinion data regardless of the search term.