Improved Tweets Polarity Detection using Lexicon-based Features and Caching
Lijo V. P.1, Hari Seetha2

1Lijo V. P.*, School of Computer Science and Engineering, Vellore Institute of Technology Vellore, India.
2Hari Seetha*, School of Computer Science and Engineering, VIT-AP, Amaravati, India.

Manuscript received on November 12, 2019. | Revised Manuscript received on 21 November, 2019. | Manuscript published on December 10, 2019. | PP: 1936-1942 | Volume-9 Issue-2, December 2019. | Retrieval Number: A5068119119/2019©BEIESP | DOI: 10.35940/ijitee.A5068.129219
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Abstract: Tweet Polarity detection the process of observing and identifying the sentiment inclination of text, whether it is positive or negative. In this paper, improved polarity detection on tweets using supervised learning is proposed. This method is using data sets available in public. The pre-processing is improved using proper caching of data items to save the time for processing of duplicate items in data sets. The feature selection strategy ensures reduced dimensionality. The low dimension data improves the classification efficiency. The experiment shows that the method is improving the overall performance in training and testing of polarity detection. 
Keywords: Sentiment Analysis, Polarity Detection, Trie, Merged Trie,
Scope of the Article: Predictive Analysis