Improving Mandarin Emotional Vocabulary in Application Writing Using Deep Learning Models
Sep 26, 2025
About this article
Published Online: Sep 26, 2025
Received: Jan 10, 2025
Accepted: May 01, 2025
DOI: https://doi.org/10.2478/amns-2025-1062
Keywords
© 2025 Lijun Feng, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
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The emotional word calculates the sample
| Candidate word | Forward frequency | Negative frequency | Threshold value=1 | Threshold value=2 | Threshold value=3 | Emotional intensity |
|---|---|---|---|---|---|---|
| Very General | 34 | 43 | -1.2438 | -2.1507 | -3.0974 | -0.6727 |
| Cure | 846 | 18 | 2.8314 | 2.0781 | 1.1103 | 0.9815 |
| Good | 693 | 14 | 2.9508 | 1.5632 | 0.6642 | 0.9286 |
| Generally | 34 | 12 | 0.4187 | -0.7638 | -1.5323 | -0.1298 |
| Chicken Soup | 30 | 61 | -1.6404 | -2.466 | -3.535 | -0.6016 |
| Embarrassing | 213 | 46 | 0.3502 | -0.5849 | -1.4594 | 0.6812 |
| Dry Goods | 33 | 27 | -0.8731 | -2.0351 | -3.0566 | -0.4729 |
| Brain Hole | 688 | 9 | 2.9753 | 2.2108 | 1.2001 | 1.1169 |
A conditional filtering process recording table based on rules
| Elimination rule | Remaining candidate affective words | Positive word | Negative term |
|---|---|---|---|
| Initial | 929229 | -- | -- |
| (1) | 90307 | 69943 | 20330 |
| (2) | 77557 | 66979 | 10585 |
| (3) | 65684 | 61547 | 8153 |
| (4)~(6) | 62930 | 55498 | 7405 |
| (7)~(9) | 59383 | 52536 | 6804 |
The emotional dictionary is the same as the result
| Categories | Dictionary size | Recall rate | Accuracy rate | F1 |
|---|---|---|---|---|
| English language dictionary | 59115 | 91.34% | 78.48% | 83.73% |
| Dalian polytechnic emotional dictionary | 27463 | 37.54% | 70.38% | 48.09% |
| The hownet emotional dictionary | 8733 | 35.34% | 75.76% | 46.92% |
The statistical phase of the statistical phase of the word frequency
| Typo | 1 star | 2 star | 4 star | 5 star | Forward | Negative direction | Total quantity |
|---|---|---|---|---|---|---|---|
| Quantity/piece | 61986 | 121715 | 973369 | 1226827 | 2285165 | 181825 | 4850887 |
Vocabulary score statistics
| Vocabulary | Book number | Number of users | 1 star | 2 star | 3 star | 4 star | 5 star |
|---|---|---|---|---|---|---|---|
| Introduction | 1207 | 1234 | 4 | 29 | 363 | 598 | 389 |
| Good | 1984 | 2217 | 21 | 57 | 370 | 1150 | 841 |
| Unintelligible | 1119 | 1315 | 250 | 349 | 359 | 281 | 165 |
| …… | 637 | 577 | 25 | 53 | 349 | 155 | 86 |
| Look at it | 814 | 1039 | 24 | 115 | 351 | 354 | 328 |
| Incomprehension | 1207 | 1234 | -1 | 29 | 363 | 598 | 389 |
High frequency emotional term top30
| Positive emotion | Good, good, good, great, great, great love, great love, great love, good, still, move, a little meaning, a little meaning, the recommendation, great, the shock, is worth reading, is worth reading, touching, is worth seeing, good, warm, very interesting |
| Negative emotion | Generally, I don’t understand, garbage, don’t like, don’t look, generally, don’t understand, see not to go, copy, still, I don’t know, I don’t read it, I don’t read it, I don’t know, it’s very general, no sense, bad, general, melodramatic, I don’t understand, I can’t read it. No meaning, tail, disappointment, Have seen, What the devil, Forget, forget, read and don’t understand, speechless |
Writing text feature statistics
| Institutional type | Text number | Basic statistics | Grammatical index | |||||
|---|---|---|---|---|---|---|---|---|
| Total word | The words are all words | It’s greater than the ratio of 6 letters | Proportional pronoun (%) | Proportional pronoun (%) | Proportional pronoun (%) | Proportional pronoun (%) | ||
| Key university | 86574 | 220.08 | 21.44 | 27.57 | 10.94 | 7.53 | 14.16 | 5.83 |
| School of our school | 602045 | 193.14 | 25.61 | 25.41 | 12.38 | 9.63 | 13.95 | 7.19 |
| Vocational school | 40405 | 188.85 | 26.43 | 30.35 | 7.41 | 7.25 | 13.74 | 9.16 |
| High school | 4042 | 140.66 | 23.66 | 22.58 | 9.67 | 6.14 | 14.62 | 3.99 |
| Junior high school | 1463 | 130.33 | 21.14 | 14.54 | 11.76 | 9.38 | 12.04 | 6.38 |
Experimental effect evaluation confusion matrix
| Forecast | |||
|---|---|---|---|
| Correctness | Errors | ||
| Actual | Correctness | TP | FN |
| Errors | FP | TN |
