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Table 1 Comparison of the techniques and approaches included in the study

From: Sentiment analysis and the complex natural language

SNo. Naïve Bayes k-Nearest neighbor Centroid
1 Yes Yes Yes
2 Yes Yes No
3 Yes No No
4 Word probability Value of k Centroid vector
5 Probability weights Distance similarity Vector distance
6 Simple and fast Handle co-related features Classify on vector distance
7 Assume feature independence Sensitive to irrelevant features Sensitive to noise
8 Yes Too expensive No
SNo. Support vector machine Lexicon (dictionary) based Statistical (corpus) based
1 Yes No No
2 No NA NA
3 No No Yes
4 Kernel function Word polarity Feature matrix
5 Hyperplane Word polarity Word distance
6 Classify on hyperplane Can identify new lexicons Handle online data
7 Require more resources Struggle with domain context Conceptual document size
8 No Yes Yes
  1. 1 require training, 2 use training data to classify, 3 probabilistic approach, 4 driving factor, 5 similarity metric, 6 strength, 7 weakness, 8 support for streaming data
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