Analyse the Sentiment of Sentences

1. Preprocess Dataset

Put your training CSV data called "training.csv" under folder "data". It is required that the data must contain columns named "text", storing the texts, and "object", a number valued 0(negative)/1(positive) representing the sentiment of the corresponding texts.

Modify "DATASET_COLUMNS" in line 11 of project/clean.py according to your dataset.

DATASET_COLUMNS = ["target", "id", "date", "flag", "user", "text"]

Run project/clean.py and you will get "tweets_processed.csv" under folder "data".

cd project
python clean.py

2. Train Models

Create a folder called "models" under "project" folder. Run project/model.py. You will get 4 models under folder "project/models".

mkdir models
python model.py

3. Predict the Testcases

Modify the testcases in file project/predict.py as you like. Run project/predict.py. You will see the predicted sentiment of your texts.

if __name__ == '__main__':
    predict("I feel happy")
    predict("I feel sad")
    predict("i don't know what i'm doing")

python predict.py

score:  [0.709203]
label:  Positive
score:  [0.00252197]
label:  Negative
score:  [0.37370038]
label:  Negative

Related Repository

SpotLight - A sentiment analysis web system using Django

Local Python and Packages Version

Note that the versions of package "tensorflow" and "keras" and the version of Python should fit according to https://docs.floydhub.com/guides/environments/.

victor@ColedeMBP:~/PycharmProjects/AppliedTextMining(master⚡) » python --version
Python 3.6.0
victor@ColedeMBP:~/PycharmProjects/AppliedTextMining(master⚡) » pip list
Package                  Version     
------------------------ ------------
absl-py                  0.9.0       
asgiref                  3.2.7       
astor                    0.8.1       
boto3                    1.12.32     
botocore                 1.15.32     
cachetools               4.0.0       
certifi                  2019.11.28  
chardet                  3.0.4       
Django                   3.0.5       
docutils                 0.15.2      
gast                     0.3.3       
gensim                   3.8.1       
google-api-core          1.16.0      
google-auth              1.12.0      
google-cloud-core        1.3.0       
google-cloud-storage     1.26.0      
google-resumable-media   0.5.0       
googleapis-common-protos 1.51.0      
grpcio                   1.27.2      
h5py                     2.10.0      
idna                     2.9         
jmespath                 0.9.5       
joblib                   0.14.1      
Keras                    2.2.4       
Keras-Applications       1.0.8       
Keras-Preprocessing      1.1.0       
Markdown                 3.2.1       
nltk                     3.4.5       
numpy                    1.18.2      
pandas                   0.25.3      
pip                      20.0.2      
protobuf                 3.11.3      
pyasn1                   0.4.8       
pyasn1-modules           0.2.8       
python-dateutil          2.8.1       
pytz                     2019.3      
PyYAML                   5.3.1       
requests                 2.23.0      
rsa                      4.0         
s3transfer               0.3.3       
scikit-learn             0.22.2.post1
scipy                    1.4.1       
setuptools               46.1.3      
six                      1.14.0      
sklearn                  0.0         
smart-open               1.10.0      
sqlparse                 0.3.1       
tensorboard              1.12.2      
tensorflow               1.12.0      
termcolor                1.1.0       
urllib3                  1.25.8      
Werkzeug                 1.0.0       
wheel                    0.34.2