@article{2278,
  author = {Wareesa Sharif, Noor Azah Samsudin, Mustafa Mat Deris, Rashid Naseem, Muhammad Faheem Mushtaq},
  title = {Effect of Negation in Sentiment Analysis},
  journal = {International Journal of Computational Linguistics Research},
  year = {2017},
  volume = {8},
  number = {2},
  doi = {},
  url = {http://www.dline.info/jcl/fulltext/v8n2/jclv8n2_1.pdf},
  abstract = {Sentiment analysis is the process to study of people opinion, emotion and way of considering a matter and take the decision into different categorize like positive, negative and neutral in data mining. The sentiment analysis is used to find out negation within the text using Natural Language Processing rules. Our aim is to detect negation affect on consumer reviews which look like positive but exactly negative in sense. A number of different approaches have been used, but these approaches do not provide an efficient and appropriate way of calculating negation sense in sentiment analysis. The proposed modified negation approach presents a way of calculating negation identification and is helpful to improve classification accuracy. The main achievement of this approach is that it is helpful for calculating the negation in sentiment analysis without the words not, no, n’t, never etc. This method produced a significant result for review classification by accuracy, precision, and recall.},
}