Quantum mechanics

Linguistics

Philosophy

Lexical analysis

tf–idf

Computer science

Recall

Natural language processing

Physics

Phrase

Noun

Adjective

Artificial intelligence

Sentence

Information retrieval

Term (time)

Sentiment analysis

Automatic summarization

Procedia Computer Science

Abstract—The number of hotel reviews are huge and growing day by day. Many travelers rely on this review to take down their decision to book a hotel. Gather valid and useful information from a huge amount of reviews are needs that must be met. Hence, a summarize tool for hotel review is built to create a representative summary. Hotel review data growing a trend of sentiment in a range of time due to the condition and improvement at that time, so the analysis of sentiment trend is done to choose the appropriate representative review data. Then, there are two method to summarize the selected review data. First, summary was obtained from extractive method with selecting most related sentence base on its Term Frequency-Inverse Document Frequency (TF-IDF) score. Second, phrase summary style is built by pairing adjective to the nearest noun and considering the polarity. From those method, obtained Recall-Oriented Understudy for Gisting Evaluation (ROUGE)-1 recall and Bilingual Evaluation Understudy (BLEU) score respectively {0.2101and 0.7820} for first method and {0.0670 and 0.03672} for the second method. All the reviews are crawled from TripAdvisor website and have been pre-processed by segmenting/tokenization, case folding, and tagging.

Extractive Hotel Review Summarization based on TF/IDF and Adjective-Noun Pairing by Considering Annual Sentiment Trends