An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention
Abstract
:1. Introduction
2. Relate Works
2.1. Lexicon-Based Methods
2.2. Machine Learning-Based Methods
2.3. Deep Learning-Based Methods
3. Methods and Results
3.1. Personality Recognition
3.1.1. Dataset Preparation
3.1.2. Personality Lexicon Construction
- (1)
- Keyword extraction
- (2)
- Lexicon construction
3.1.3. Correlation Analysis
3.1.4. Experiments and Results
3.2. Sentiment Classification
3.2.1. P-BiLSTM-SA Model
- (1)
- BiLSTM layer
- (2)
- Self-attention layer
- (3)
- Sentiment classification
- (4)
- Ensemble of sentiment classifiers results
3.2.2. Experiments and Results
- (1)
- Data processing
- (2)
- Parameter setting
- (3)
- Experiments and discussion
4. Conclusions and Future Work
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Label | Categories | Some Keywords |
0 | Positive comments | excellent, rhythm, personality, nice, outgoing, perfect, impression, performance, sensible, mature, patient, hard-working, calm |
1 | Time | Monday, weekend, morning, next day, evening, midnight, holiday, the day before |
2 | Daily life | catch up, chat, pass by, steal away, shaking hands, shopping, smoking, discounting |
3 | Relationship | dad, sister, friend, partner, colleague, grandparents, neighbor, baby, husband, brother |
4 | Location | Shanghai, Nanjing, Jinan, Thailand, Yantai, train, Wuhan, city, weather |
5 | Cognition process | understand, choose, question, wonder, confuse, figure out, understand, realize, gradually, familiar, know |
6 | Blessing | wishes, happy birthday, good luck, smooth, family happiness, celebration, blessings, wonderful |
7 | Platform activities | help, vote, super, idol, popularity, live, red packets, cash, received, follow, surprise, opportunity |
8 | Positive emotions | go fighting, life, effort, future, life, happiness, summer, thanks, strength, beautiful, energy, youth |
9 | Body health | stomach, health care, sauna, massage, vitamins, medicine, back pain, soreness, abs, workouts |
10 | Social events | hostage, death, disease, avoidance, sentence, drugging, mediation, victimization, sexual harassment, humiliation, domestic violence |
11 | Negative comments | annoying, shady, stupid, disgusting, angry, hateful, joke, unworthy, uninstall, self-directed, haters |
12 | Work | meeting, handover, human resources, group, late, work, salary, retirement, work overtime, boss |
13 | Values | integrity, society, honor, shame, nation, spirit, culture, rights, collectivism, ideals and beliefs, moral standards, guidance, discipline |
14 | School life | college students, teachers, schools, study, homework, papers, classmates, preparation for postgraduate entrance examination, graduation, examination |
15 | Negative emotions | things, emotions, sad, experience, mood, fear, disappointment, painful, maybe, give up, anxiety, sorrow |
16 | Competition | national football team, women’s basketball team, table tennis, Olympic Games, championships, playing, running, winning, champion, gold |
17 | Foods | barley, milk, taste, hot pot, rice, orange, burger, egg, cake, coffee, delicious, seafood, milk tea |
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Personality | Mean | Standard Deviation | L (%) | M (%) | H (%) |
---|---|---|---|---|---|
A | 32.67 | 4.58 | 29.29 | 47.13 | 23.48 |
C | 27.79 | 5.08 | 19.52 | 50.66 | 29.82 |
E | 22.85 | 5.41 | 21.64 | 53.82 | 24.54 |
N | 25.90 | 4.62 | 21.90 | 57.78 | 20.32 |
O | 30.69 | 4.90 | 25.33 | 44.85 | 29.82 |
Label | Categories | A | C | E | N | O |
---|---|---|---|---|---|---|
0 | Positive comments | 0.017 * | −0.019 | −0.02 * | 0.014 ** | 0.012 |
1 | Time | 0.02 * | 0.049 ** | 0.053 | −0.07 | −0.041 |
2 | Daily life | 0.081 | 0.04 | 0.031 | 0.049 * | 0.096 |
3 | Relationship | −0.055 | 0.039 | 0.053 ** | 0.012 | −0.108 |
4 | Location | −0.07 | 0.021 | −0.073 | 0.016 | 0.153 *** |
5 | Cognitive processes | −0.085 | 0.029 | −0.001 | 0.004 * | 0.052 ** |
6 | Blessing | 0.081 * | −0.048 | −0.08 | −0.078 | −0.013 |
7 | Platform activities | 0.015 | −0.101 | 0.012 ** | −0.012 | 0.084 |
8 | Positive emotions | 0.064 | −0.027 * | 0.065 | 0.013 * | −0.039 |
9 | Body health | 0.057 * | 0.072 | −0.06 * | 0.06 * | 0.101 ** |
10 | Social events | −0.105 | 0.031 | −0.024 ** | 0.041 * | 0.078 |
11 | Negative comments | −0.054 ** | −0.05 * | −0.069 | 0.055 ** | −0.025 |
12 | Work | −0.026 *** | 0.077 ** | −0.014 | −0.005 | −0.08 |
13 | Values | −0.015 ** | 0.021 | −0.031 * | −0.027 | 0.044 ** |
14 | School life | 0.035 * | 0.021 * | 0.057 ** | 0.009 | 0.043 |
15 | Negative emotions | −0.089 * | 0.032 | −0.044 | 0.018 *** | −0.073 |
16 | Competition | 0.129 * | −0.11 | 0.035 | 0.015 | 0.057 |
17 | Foods | 0.041 | 0.055 | 0.034 | 0.029 | 0.069 *** |
Lexicon | A | C | E | N | O |
---|---|---|---|---|---|
Personality lexicon | 0.7281 | 0.7075 | 0.6994 | 0.6852 | 0.7335 |
SCLIWC | 0.7153 | 0.6971 | 0.6791 | 0.6634 | 0.7028 |
Lexicon | Model | A | C | E | N | O | Average |
---|---|---|---|---|---|---|---|
Personality lexicon | RF | 0.7366 | 0.7148 | 0.6379 | 0.6287 | 0.6952 | 0.6826 |
SVM | 0.6813 | 0.6795 | 0.6514 | 0.6162 | 0.6793 | 0.6615 | |
NB | 0.6781 | 0.6743 | 0.6335 | 0.6095 | 0.6775 | 0.6546 | |
SCLIWC | RF | 0.6586 | 0.6539 | 0.6185 | 0.5839 | 0.6007 | 0.6231 |
SVM | 0.6437 | 0.6641 | 0.5972 | 0.6094 | 0.594 | 0.6217 | |
NB | 0.6211 | 0.6363 | 0.5664 | 0.5667 | 0.5771 | 0.5935 |
Dimensions | A | C | E | N | O |
---|---|---|---|---|---|
low | 301 | 421 | 241 | 197 | 252 |
middle | 45 | 95 | 29 | 220 | 70 |
high | 387 | 217 | 463 | 316 | 411 |
Parameters | Values | Parameters | Values |
---|---|---|---|
Bach-size | 128 | Dropout | 0.5 |
Hidden_size | 128 | lr | 0.001 |
Att_size | 100 | Epochs | 300 |
classifiers | HA | HC | HE | HN | HO | ALL |
accuracy | 0.7484 | 0.7218 | 0.7408 | 0.7382 | 0.7505 | 0.7315 |
classifiers | LA | LC | LE | LN | LO | P-BiLSTM-SA |
accuracy | 0.7351 | 0.7360 | 0.7461 | 0.7297 | 0.7368 | 0.8156 |
Index | P-BiLSTM-SA | −A | −C | −E | −N | −O |
---|---|---|---|---|---|---|
Accuracy | 0.8156 | 0.7932 | 0.7850 | 0.7842 | 0.8026 | 0.8002 |
F1-score | 0.7945 | 0.7821 | 0.7742 | 0.7751 | 0.7693 | 0.7798 |
Model | Accuracy | Recall | Precision | F1-Score |
---|---|---|---|---|
BiLSTM-SA | 0.7658 | 0.7037 | 0.7647 | 0.7329 |
P-LSTM | 0.7880 | 0.7266 | 0.7929 | 0.7583 |
P-BiLSTM | 0.7937 | 0.7300 | 0.7978 | 0.7624 |
BiLSTM + EMB-ATT | 0.7818 | 0.7485 | 0.7804 | 0.7641 |
EMCNN | 0.7934 | 0.7427 | 0.8003 | 0.7704 |
P-BiLSTM-SA | 0.8156 | 0.7425 | 0.8544 | 0.7945 |
Model | Accuracy | Recall | Precision | F1-Score |
---|---|---|---|---|
BiLSTM-SA | 0.7709 | 0.7137 | 0.7708 | 0.7412 |
P-LSTM | 0.8164 | 0.7431 | 0.8192 | 0.7793 |
P-BiLSTM | 0.8190 | 0.7448 | 0.8218 | 0.7814 |
BiLSTM + EMB-ATT | 0.7921 | 0.7849 | 0.7911 | 0.7880 |
EMCNN | 0.8211 | 0.8184 | 0.8035 | 0.8109 |
P-BiLSTM-SA | 0.8288 | 0.8486 | 0.8274 | 0.8379 |
No. | Personalities | Weibo Texts | P-BiLSTM-SA |
---|---|---|---|
(1) | HC HA HO | Self-discipline makes sport purer. Tomorrow is the marathon, why am I feel more excited than I imagined? | Positive |
(2) | LE HC LO | Actually, I can’t pinpoint the specific reason why, even though I have no worries about food and clothing and have a job, I just feel exhausted, a kind of exhaustion that seems unsolvable. | Negative |
(3) | HE HC HN | I finally solved it, I am so awesome, it’s incredible. | Positive |
(4) | LE LO HN | Feeling exhausted and in pain, enduring for the sake of those fleeting moments of happiness, damn. | Negative |
(5) | LA LO HN | You’re amazing, hehe. | Negative |
(6) | LA LE | Living well, earning money, not starving, and going wherever you want. | Positive |
(7) | HA HE | When you know what you want, you won’t feel lost. | Positive |
(8) | HN LE HO | Living in constant self-doubt, self-denial, self-encouragement, and self-redemption every day. | Negative |
(9) | HO HE HA | Shocked! A female college student spent the Qingming holiday watching the replay of a delicious roasted lamb leg live stream in her dorm instead of going out to enjoy the spring scenery! | Negative |
(10) | LC LO LE | No desire to study... | Negative |
(11) | LN HO HC HE HA | Some recent fragments—a regular life is really nice. I haven’t had insomnia lately! The food at the third cafeteria is really delicious! Ordering three dishes for two people is super cost-effective and we can eat until we’re full. | Positive |
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Liu, K.; Feng, Y.; Zhang, L.; Wang, R.; Wang, W.; Yuan, X.; Cui, X.; Li, X.; Li, H. An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention. Electronics 2023, 12, 3274. https://doi.org/10.3390/electronics12153274
Liu K, Feng Y, Zhang L, Wang R, Wang W, Yuan X, Cui X, Li X, Li H. An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention. Electronics. 2023; 12(15):3274. https://doi.org/10.3390/electronics12153274
Chicago/Turabian StyleLiu, Kejian, Yuanyuan Feng, Liying Zhang, Rongju Wang, Wei Wang, Xianzhi Yuan, Xuran Cui, Xianyong Li, and Hailing Li. 2023. "An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention" Electronics 12, no. 15: 3274. https://doi.org/10.3390/electronics12153274
APA StyleLiu, K., Feng, Y., Zhang, L., Wang, R., Wang, W., Yuan, X., Cui, X., Li, X., & Li, H. (2023). An Effective Personality-Based Model for Short Text Sentiment Classification Using BiLSTM and Self-Attention. Electronics, 12(15), 3274. https://doi.org/10.3390/electronics12153274