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Text Mining and Data Information Analysis for Network Public Opinion Cover

Text Mining and Data Information Analysis for Network Public Opinion

By: Yan Hu  
Open Access
|Jan 2019

Figures & Tables

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Figure 1

The flow of PMML.

Table 1

Matching patterns of phases.

PatternCollocationsExample
E1Adjective + nounIntimate service
E2Adjective + adverbHigh enough
E3Adjective + verbEfficient operation
E4Noun + adjectiveAllocation perfect
E5Adverb + adjectiveEspecially like
E6Negative word + verbNot satisfactory
E7Negative word + adjectiveNot beautiful
E8Negative word + adverb + verbNot very like
E9Verb + adjectiveConsume fast
E10Adverb + negative word + verbNot happy
Table 2

The classification situation.

Actual number of texts which belong to the classActual number of texts which do not belong to the class
Number of texts which are identified as the class by the classifierwx
Number of texts which are identified not as the class by the classifieryz
Table 3

The comparison of classification performance of N-Gram.

FeatureUniGramsBiGramsTriGrams
PP62.72%67.62%65.85%
PR54.27%47.55%43.72%
NP60.38%63.82%59.32%
NR72.54%76.41%64.78%
Table 4

The comparison of classification performance of PMML.

FeaturePMML + UniGramsPMML + BiGramsPMML + TriGrams
PP82.15%86.75%78.25%
PR80.53%83.49%76.43%
NP82.78%84.64%80.59%
NR79.21%78.21%77.67%
Table 5

The classification of emotional tendency of comment texts.

CategoryNumber (n)Percentage (%)
Positive (commendatory) emotional tendency276435.02
Negative (derogatory) emotional tendency509964.61
Not classified290.37
Language: English
Submitted on: Nov 2, 2018
Accepted on: Jan 4, 2019
Published on: Jan 23, 2019
Published by: Ubiquity Press
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Yan Hu, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.