Semi-Supervised Keyphrase Extraction on Scientific Article using Fact-based Sentiment

Based on Sentiment Analysis module on Stanford CoreNLP, the first sentence is assigned as 3 (positive) whereas the second one is assigned as 1 (negative). [...]fact-based sentiment value of Artificial Neural Network will be (3 + 1)/2 = 2 (neutral). [...]CD is generated by excluding human-tagged keyphrases that are not found on article content or are not recognized as keyphrase candidate through proposed candidate selection heuristic. [...]it can be stated that some keyphrases are not found on article content or excluded as the result of candidate selection heuristics. [...]it can be stated that our approach is moderately effective considering most keyphrase extraction approaches generate similar F-measure [34].