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related keywords as representative vectors for different sentiments, we use these vectors as the sentiment classifier for the testing set. We achieved results that are not only comparable to traditional methods like Naïve Bayes and SVM, but also outperform Latent Dirichlet Allocation, TF-IDF and its variant. It also
approach involves the detection and use of self-defining features that are available within the data. We take into account two emotionally rich features: a) emoticons and b) lists of emotionally intense keywords. These features are evaluated on data coming from a popular forum, using various classifiers and feature vectors
temporal changes of brand-related keyword networks. Our analysis enables trends in brand awareness to be systematically traced and evaluated. This allows various other analyses, such as advantages and disadvantages of the brand, and a comparison with its competitors.
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
enter the keywords that describe the topic of interest and to present the results in several levels of detail. To meet the second requirement, the system uses a Naïve Bayes classifier to identify the sentiments of tweets, as the literature shows that this algorithm combines a good classificatory performance and low
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