EMOSIS Sentiment Analysis on Tweets with Emotion and Intensity Level Recognition Considering Ending Punctuation Marks
Ria Ambrocio Sagum1, Ma. Monique L. Navarro2, Arvin Jasper E. Victore3
1Ria Ambrocio Sagum*, College of Computer and Information Sciences, Polytechnic University of the Philippines, Manila, Philippines.
2Monique L. Navarro, College of Computer and Information Sciences, Polytechnic University of the Philippines, Manila, Philippines.
3Arvin Jasper E. Victore College of Computer and Information Sciences, Polytechnic University of the Philippines, Manila, Philippines.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 24 2019. | Manuscript published on 30 November, 2019. | PP: 10289-10293 | Volume-8 Issue-4, November 2019. | Retrieval Number: D4518118419/2019©BEIESP | DOI: 10.35940/ijrte.D4518.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment Analysis is a tool used for determining the Polarity or Emotion of a Sentence. It is a field of Natural Language Processing which focuses on the study of opinions. In this study, the researchers solved one key challenge in Sentiment Analysis, which is to consider the Ending Punctuation Marks present in a sentence. Ending punctuation marks plays a significant role in Emotion Recognition and Intensity Level Recognition. The research made used of tweets expressing opinions about Philippine President Rodrigo Duterte. These downloaded tweets served as the inputs. It was initially subjected to pre-processing stage to be able to prepare the sentences for processing. A Language Model was created to serve as the classifier for determining the scores of the tweets. The scores give the polarity of the sentence. Accuracy is very important in sentiment analysis. To increase the chance of correctly identifying the polarity of the tweets, the input undergone Intensity Level Recognition which determines the intensifiers and negations within the sentences. The system was evaluated with overall performance of 80.27%.
Keywords: Sentiment Analysis; Emotion Recognition; Intensity Level Recognition, Polarity of Sentence
Scope of the Article: Pattern Recognition and Analysis.