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We propose a feature word selection method for classifying recommended shops using Yelp customer reviews. TextRank keywords are extracted from the customer reviews to construct the sorted positive and negative keyword lists based on each keyword's summed TextRank scores. The top-K keywords are then aggregated
Sentiment analysis in text mining is known to be a challenging task. Sentiment is subtly reflected by the tone, affective state or emotion of a writer's expression in words. Conventional text mining techniques which are based on keyword frequency counting usually run short of accurately detecting such subjective
detect user sentiments. The keyword-based approaches for identifying such themes fail to give satisfactory level of accuracy. Here, we address the above problems using statistical text-mining of blog entries. The crux of the analysis lies in mining quantitative information from textual entries. Once the relevant blog
favorite restaurant. The sentiment analysis for restaurant rating system rates the restaurant depending upon the reviews given by the users. The system breaks user comments to check for sentiment keywords. Once the keywords are found, it associates the comment with a sentiment rank. Sentiment analysis can also be extended
better service quality. This study aims to measure GO-JEK and Grab customer satisfaction through sentiment analysis of Twitter's data. Both companies use Twitter to reach their customers and promote their service. We collect 126,405 tweets from February to March 2016 containing GO-JEK and Grab keywords. Then, we pre-process
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