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Research on Red Cultural Inheritance and Application of SVM Support Vector Machine in Sentiment Analysis

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24 mars 2025
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Figure 1.

The overall process of text emotional classification
The overall process of text emotional classification

Figure 2.

Based on the SVM’s red cultural heritage tendency prediction model
Based on the SVM’s red cultural heritage tendency prediction model

Figure 3.

Comparison of the classification results
Comparison of the classification results

Figure 4.

Daily comments on the number of emotional trends
Daily comments on the number of emotional trends

Partial emotional dictionary screenshot

Word Lexical Meaning Class number Affective classification Strength Polarity
Dingy adj 1 1 NN 7 2
Premature failure adj 1 1 NE 5 1
Reprove verb 1 1 NN 5 2
Thief eye noun 1 1 NN 4 2
War noun 1 1 ND 3 2
Clear roughness adj 1 1 PH 5 0
Limpid adj 1 1 PH 5 1

Information collection details

Date Positive Slightly positive Neutrality Slightly negative Negative Total Daily accuracy
1 5/1 495 554 215 365 456 2085 89.824
2 5/2 635 512 123 214 424 1908 87.073
3 5/3 726 531 116 135 359 1867 89.364
4 5/4 615 193 154 256 461 1679 87.221
5 5/5 232 472 168 413 547 1832 89.307
6 5/6 425 268 121 149 132 1095 85.226
7 5/7 546 409 137 335 191 1618 88.919
8 5/8 387 514 256 215 185 1557 85.423
9 5/9 299 327 172 412 198 1408 87.341

Internet comment high frequency vocabulary

Number Key words Serial number Number Key words Serial number
1 Youth talk 854 11 Youth dream 132
2 Holiday 792 12 Red culture 102
3 Youth 654 13 Cultural heritage 86
4 Youth festival 412 14 Youth education 81
5 May 4 commemorative activities 301 15 Traditional festival 75
6 May Fourth movement 256 16 Origin of festival 72
7 The origin of youth festival 221 17 Theme activity 69
8 A message of the youth day 217 18 Historical figure 64
9 Youth activity 185 19 Historical event 52
10 The history of the May 4 movement 164 20 Youth image 37