https://github.com/jayfunc/Sentiment-Analysis
Using Dart and Python to build a sentiment analysis Flutter app - 基于 Dart 语言构建的 Flutter 情感分析应用
https://github.com/jayfunc/Sentiment-Analysis
dart flutter machine-learning sentiment-analysis
Last synced: 8 months ago
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Using Dart and Python to build a sentiment analysis Flutter app - 基于 Dart 语言构建的 Flutter 情感分析应用
- Host: GitHub
- URL: https://github.com/jayfunc/Sentiment-Analysis
- Owner: jayllfilebyte
- License: apache-2.0
- Created: 2023-10-18T07:54:38.000Z (over 2 years ago)
- Default Branch: master
- Last Pushed: 2023-12-07T14:55:28.000Z (over 2 years ago)
- Last Synced: 2025-01-13T02:38:47.670Z (over 1 year ago)
- Topics: dart, flutter, machine-learning, sentiment-analysis
- Language: C++
- Homepage:
- Size: 1.64 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Documentation for Sentiment Analysis
> [!WARNING]
> If anyone who want to copy this project as his or her course project, do not contact me for further help.
## Project Overview
This is a final project for Data Mining course.
### Project Goal
- Detect aggressive comments in public communities, like YouTube, Twitter, etc.
- Detect fake news in public media platform.
### Language
- Dart for both ML(Machine Learning) algorithm and UI(User Interface).
## Data Description
### Datasets (from [Kaggle](https://www.kaggle.com/))
- [Cleaned Toxic Comments](https://www.kaggle.com/datasets/fizzbuzz/cleaned-toxic-comments?select=train_preprocessed.csv)
- [Clean Fake News Dataset](https://www.kaggle.com/datasets/rakeshsahni/clean-fake-news-dataset?select=fake_news_train_clean.csv)
### Pre-processing
In this section, we will only give you an example for `Cleaned Toxic Comments` datasets since all the dataset have the same steps to be processed.
#### Select important columns
We only need `comment_text` and `insult` columns since the other columns(attributes) are not very important.
Below is the code:
```dart
DataFrame dataFrame = await fromCsv(
'data/normal_offensive_data.csv',
columns: [0, 3]);
```
where `0` is the index of `comment_text` column and `3` is the index of `insult` column.
#### Sampling
We only collect 10% of the whole dataset to train our model to speed up the time of building model.
Here is the code:
```dart
List sampleCommentIndexes = [];
for (var i = 0; i < dataFrame.rows.length; i++) {
if (i % sampleStep == 0) {
sampleCommentIndexes.add(i);
}
}
dataFrame = dataFrame.sampleFromRows(sampleCommentIndexes);
```
where `sampleStep` = 10.
#### Text vectorization
After reducing the dimensions of dataset, we feed machine dataset like this:
```
flutter: DataFrame (159571 x 2)
comment_text insult
explanation why the edits ... 0.0
... ...
d aww he matches this ba ... 0.0
```
where `comment_text` is the comment that user posted and `insult` indicated whether this content was insult-able or not.
Then the content in every `comment_text` will be sent to a [Python program](https://github.com/founchoo/doc2vec_server)(written by ourselves) by HTTP `GET` method and the response will be like this:
```
[
-0.7947729229927063,
-0.3663290739059448,
-0.6826316714286804,
0.5298082828521729,
0.38561514019966125,
...
0.8614583015441895
]
```
This array is the vectorized result of the content in single `comment_text`.
Here, when the Python server receives the `GET` request, it will convert `String` text to `List` vector by using BERT model in [huggingface](https://huggingface.co/bert-base-uncased) with [TensorFlow](https://www.tensorflow.org/) framework.
After vectorization, the result will be like this:
```
flutter: Pre-processing result:
flutter: DataFrame (12689 x 769)
insult fea_0 fea_1 fea_2 fea_3 fea_4 ... fea_767
0.0 -0.7947729229927063 -0.3663290739059448 -0.6826316714286804 0.5298082828521729 0.38561514019966125 ... 0.8614583015441895
0.0 -0.6552587151527405 -0.5079750418663025 -0.9163077473640442 0.6903209686279297 0.7387704253196716 ... 0.6643986105918884
0.0 -0.7848144173622131 -0.4483385980129242 -0.904833972454071 0.7669529914855957 0.7172573208808899 ... 0.7397924065589905
0.0 -0.8217465281486511 -0.47220537066459656 -0.8824613690376282 0.7397663593292236 0.5861581563949585 ... 0.8556132316589355
1.0 -0.5986714363098145 -0.2154504358768463 0.4265202283859253 0.27244287729263306 -0.1582547426223755 ... 0.6849899291992188
... ... ... ... ... ... ... ...
0.0 -0.8255961537361145 -0.4590681493282318 -0.5918567776679993 0.7359922528266907 0.3755975067615509 ... 0.901710569858551
0.0 -0.731025218963623 -0.43930941820144653 -0.8263741135597229 0.638615608215332 0.3906306028366089 ... 0.7427312731742859
0.0 -0.7566186189651489 -0.4142800569534302 -0.7555342316627502 0.5424911379814148 0.40552330017089844 ... 0.7499610781669617
1.0 -0.7954379916191101 -0.47804439067840576 -0.79896080493927 0.6801205277442932 0.6424688100814819 ... 0.8404900431632996
0.0 -0.7470123171806335 -0.5245763659477234 -0.8404871225357056 0.6709993481636047 0.7077993154525757 ... 0.7762796878814697
flutter: Pre-processing done in 0:43:46.870327
```
## Data Mining Technologies
### Machine Learning
We use supervised learning that makes use of class labels to predict information.
## Model Building
We choose [KnnClassifier](https://pub.dev/documentation/ml_algo/latest/ml_algo/KnnClassifier-class.html) algorithm to build classification model.
To build model, just call:
```dart
model = await core.buildModel(dataFrame);
```
## Evaluation Metrics
Predict the dataset with training data removed, then calculate the correction rate.
## Results and Findings
According to the metric above, our model has a correction rate between 65% and 70%.
## Limitations
Because of the limitation of BERT model, we can only handle text with limited word size. We have tested that the size of 600(the length of a `String`) was acceptable.
## Reproducibility
> [!IMPORTANT]
> We do not upload the files below:
> - normal_offensive_model.json
> - normal_offensive_data.csv
> - real_fake_model.json
> - real_fake_data.csv
>
>
> Follow steps from 3 to 5 to ensure your `.csv` files are ready to be processed.
> The `.json` files are generated by program, those files will be generated when you finish traning your model.
1. Clone [doc2vec_server](https://github.com/founchoo/doc2vec_server) repository and run it in the background.
2. Clone this repository.
3. Create `data` folder in root directory of your cloned project.
4. Download datasets from [here](https://github.com/founchoo/sentiment_analysis?tab=readme-ov-file#datasets-from-kaggle) and put them in `data` folder.
5. Rename those two downloaded files to `normal_offensive_data.csv` and `real_fake_data.csv` respectively.
6. Run `flutter run` in the root directory.
## Data Privacy and Ethical Considerations
All datasets come from [Kaggle](https://www.kaggle.com/), which are public datasets.
The contents in dataset may contain some aggressive words.
## References
Thanks to the following libraries/websites, we could finish our project successfully.
- [sentiment_dart](https://pub.dev/packages/sentiment_dart), it gives us an inspiration on how to achieve our goal at the very beginning.
- [ml_dataframe](https://pub.dev/packages/ml_dataframe), it provides a way to store and manipulate data.
- [ml_algo](https://pub.dev/packages/ml_algo), it provides lots of algorithms for ML.
- [stopwordies](https://pub.dev/packages/stopwordies), it provides English stop words, which can be used to identify relatively meaningless words in a sentence.
- [document_analysis](https://pub.dev/packages/document_analysis), it provides text vectorization method and gives us some ideas on how to vectorize text at the first.
- [tutorial](https://www.kaggle.com/code/ashokkumarpalivela/sentiment-analysis-with-machine-learning/notebook), it provides guideline on how to apply ML in Python and gives us a structure to follow.
## Future Works
It is not very efficient to send every single `comment_text` to Python server and wait for the response.
Although there is no doc2vec libraries in Dart, we find there is an official document provides a [guide](https://huggingface.co/docs/optimum/exporters/tflite/usage_guides/export_a_model) on how to convert TensorFlow model to TensorFlow Lite model, and luckily there is a library called [tflite_flutter_plus](https://pub.dev/packages/tflite_flutter_plus) providing a interpreter between TensorFlow Lite and Dart.
So maybe we can convert the doc2vec model to TensorFlow Lite model and use it in Dart directly.
## Presentation and Visualization
We presented it with PowerPoint, but we won't upload this file.