https://github.com/zeuscoderbe/nlp-clustering-word--vietnamese-sentiment-analysis
NLP-clustering(word) -Vietnamese Sentiment Analysis using artificial neural network
https://github.com/zeuscoderbe/nlp-clustering-word--vietnamese-sentiment-analysis
cnn-text-classification deep-learning generative-ai lstm-neural-networks machine-learning nlp-machine-learning sentiment-analysis
Last synced: about 1 month ago
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NLP-clustering(word) -Vietnamese Sentiment Analysis using artificial neural network
- Host: GitHub
- URL: https://github.com/zeuscoderbe/nlp-clustering-word--vietnamese-sentiment-analysis
- Owner: ZeusCoderBE
- Created: 2024-04-27T04:22:10.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-12T17:09:01.000Z (5 months ago)
- Last Synced: 2025-03-26T17:56:51.376Z (about 2 months ago)
- Topics: cnn-text-classification, deep-learning, generative-ai, lstm-neural-networks, machine-learning, nlp-machine-learning, sentiment-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 204 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- Changelog: history/history_hybrid_cnn.pkl
Awesome Lists containing this project
README
### Workflow: Applying artificial neural networks to build models to analyze customer emotions based on comments and evaluation serves for determination business-related trends
### I.Data Information:
- **Train Data:** 7,786 samples
- **Dev**: 1,112 samples
- **Test Data:** 2,224 samples
- **Link:** [Dataset Repository](https://github.com/LuongPhan/UIT-ViSFD?tab=readme-ov-file)
### II.Attribute Information:
1. **id** : the id of comment
2. **comment:** Commentary content
3. **n_star:** User rating for the smartphone (stars)
4. **data_time:** Date and time the comment was posted
5. **label:** Sentiment label of the comment
### III.Inferential analysis and exploratory analysis
##### 1.The chart shows customers' ratings of products over time
###### Based on the chart above, in the years from early 2017 to early 2019, customer reviews for products were quite high, averaging about 4.5. But between mid-2019 and the end of 2020, average reviews dropped alarmingly, demonstrating that customers are very dissatisfied with the quality of our products or services. It is necessary to urgently re-check product or service quality management steps to improve the situation
##### 2.The chart shows the number of labels evaluated over time
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###### We can see that the number of Positive Reviews is always more than other categories. Another notable point is that from early 2019 to mid-2020, the number of classified ad reviews increased rapidly, meaning the number of customers skyrocketed during that time.
##### 3.The line graph shows the number of reviews for each status by word count
###### It is observed that users tend to use less than 40 words to rate. The number of Positive reviews is always higher than Negative, this is a good sign for the product business.
##### 4.The heatmap chart represents the correlation matrix for the columns positive count, negative count, neutral_count, n_star
##### The chart shows that the positive_count value is positively correlated with n_star (correlation index 0.65), meaning that in user reviews, the higher the number of positive_count words, the higher the likelihood that that user will give a high rating. On the contrary, the negative_count value is negatively correlated with n_star (correlation index -0.69), meaning that in user reviews, the more negative_count words there are, the lower the rating will be.
### IV.Visualize word context and semantic correlation
### 5.1.Ploting learning curves BERT-fine-tuned for sentiment analysis:
### 5.2.Ploting learning curves LSTM for sentiment analysis:
### 5.3.Ploting learning curves Hybrid CNN with LSTM for sentiment analysis:
