ISSN 2394-5125
 

Research Article 


An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis

S.Kasthuri, Dr.A.Nisha Jebaseeli.

Abstract
Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. The huge amount of information from this medium has become an attractive resource for organizations to monitor the opinions of users, and therefore, it is receiving a lot of attention in the field of sentiment analysis. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the twitter dataset. After collecting the data, pre-processing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of two systems; lemmatization, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted key-words were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Decision Tree (DT). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

Key words: Sentiment Analysis, Decision Tree, Twitter, Lemmatization, Latent Dirichlet Allocation.


 
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How to Cite this Article
Pubmed Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. JCR. 2020; 7(4): 1010-1018. doi:10.31838/jcr.07.04.190


Web Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. http://www.jcreview.com/?mno=99122 [Access: April 18, 2021]. doi:10.31838/jcr.07.04.190


AMA (American Medical Association) Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. JCR. 2020; 7(4): 1010-1018. doi:10.31838/jcr.07.04.190



Vancouver/ICMJE Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. JCR. (2020), [cited April 18, 2021]; 7(4): 1010-1018. doi:10.31838/jcr.07.04.190



Harvard Style

S.Kasthuri, Dr.A.Nisha Jebaseeli (2020) An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. JCR, 7 (4), 1010-1018. doi:10.31838/jcr.07.04.190



Turabian Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. 2020. An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. Journal of Critical Reviews, 7 (4), 1010-1018. doi:10.31838/jcr.07.04.190



Chicago Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. "An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis." Journal of Critical Reviews 7 (2020), 1010-1018. doi:10.31838/jcr.07.04.190



MLA (The Modern Language Association) Style

S.Kasthuri, Dr.A.Nisha Jebaseeli. "An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis." Journal of Critical Reviews 7.4 (2020), 1010-1018. Print. doi:10.31838/jcr.07.04.190



APA (American Psychological Association) Style

S.Kasthuri, Dr.A.Nisha Jebaseeli (2020) An efficient Decision Tree Algorithm for analyzing the Twitter Sentiment Analysis. Journal of Critical Reviews, 7 (4), 1010-1018. doi:10.31838/jcr.07.04.190