SENTIMENT SCORES OF SENTIMENT KEYWORDS: ANALYSIS OF HOTEL REVIEW DATA

Abstract

Utilizing hotel review data by analyzing specific keywords as either being positive or negative, it is possible to aid consumers in selecting a suitable hotel. For this purpose, we propose creating a Sentiment Score of each Sentiment Keyword. We first construct a Sentiment Lexicon by extracting Sentiment Keywords by natural language processing. Afterward, we formulate a coefficient between the Sentiment Keyword and the evaluation score through ridge regression. The coefficient is then normalized to arrive at the cumulative probability of normal distribution, ultimately employed as the Sentiment Score of the specific Sentiment Keyword. However, if the coefficient is considered to be insignificant, the Sentiment Score is replaced by the probability of the Sentiment Keyword as either being positive or negative by using label propagation with the k -Nearest Neighbor ( k -NN) classifier. The data analyzed is focused on South Korean hotel reservation websites.