2017
DOI: 10.5815/ijisa.2017.07.07
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Sentiment Predictions using Support Vector Machines for Odd-Even Formula in Delhi

Abstract: This paper analyzes the odd-even formula in Delhi using tweets posted on Twitter from December 2015 to August 2016. Twitter is a social network where users post their feelings, opinions and sentiments for any event using hashtags and mentions. The tweets posted publicly can be viewed by anyone interested. This paper transforms the unstructured tweets into structured information using open source libraries. Further objective is to build a model using Support Vector Machines (SVM) to classify unseen tweets on th… Show more

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Cited by 11 publications
(8 citation statements)
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“…In Table 4 and 5, the class label corresponding to number 1 was positive, 2 was negative and 3, neutral. Various evaluation parameters were obtained such as true positive rate (TPR), false positive rate (FPR), Precision (% of selected items that are correct), Recall (% of correct items that are selected) [24] and ROC (receiver operating characteristic curve) [35] etc. Figure 3 and Figure 4 illustrates the Error measure comparison and Fmeasure for various classifiers in WEKA [33].…”
Section: Resultsmentioning
confidence: 99%
“…In Table 4 and 5, the class label corresponding to number 1 was positive, 2 was negative and 3, neutral. Various evaluation parameters were obtained such as true positive rate (TPR), false positive rate (FPR), Precision (% of selected items that are correct), Recall (% of correct items that are selected) [24] and ROC (receiver operating characteristic curve) [35] etc. Figure 3 and Figure 4 illustrates the Error measure comparison and Fmeasure for various classifiers in WEKA [33].…”
Section: Resultsmentioning
confidence: 99%
“…The process of classification algorithms utilized in this study is explained below. The process is sourced and mapped from the study of Sharma and Hoque [15]:  Extract the relevant tweets from the obtained datasets.  Given a set of tweets, estimate tweets' distribution and classify them into positive, negative, and neutral classes based on terrorism content.…”
Section: A Classification Algorithmsmentioning
confidence: 99%
“…Furthermore, in [24] they built IQAS that has the ability to process queries in the form of factoid and non-factoid question. If the question was a factoid question, it used Named Entity Recognition (NER) and SVM [50][51] to process it. Vice versa, pattern matching and semantic analysis methods were used for non-factoid question.…”
Section: State Of the Art In Indonesian Questionmentioning
confidence: 99%