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Title |
SEQUENTIAL MINIMAL OPTIMIZATION WITH RANDOM FOREST ALGORITHM (SMORF) USING TWITTER CLASSIFICATION TECHNIQUES |
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Author |
J.Uma and Dr.K.Prabha |
| Citation |
Vol. 23 No. 4 pp. 116-122 |
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Abstract |
Sentiment categorization technique be commonly isolated interested in threes significant classifications name Machine Learning Procedure (ML), Lexicon Based Method (LB) also finally, the Hybrid Method. In Machine Learning Methods (ML) utilizes phonetic highlights with apply notable ML algorithm. In this paper, in classification and identification be complete base under in optimizations technique called sequential minimal optimization with Random Forest algorithm (SMORF) for expanding the exhibition and proficiency of sentiment classification framework. The three existing classification algorithms are compared with proposed SMORF algorithm. Imitation result within experiential structure is Precisions (P), recalls (R), F-measures (F) and accuracy metric. The proposed sequential minimal optimization with Random Forest (SMORF) provides the great accuracy. |
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Keywords |
Sentiment categorization, Machine Learning Procedure, Lexicon Based Procedure, Random Forest algorithm, sequential minimal optimization, Twitter |
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