NLP text analysis demo

The project uses Apache Open NLP library for simple text analysis (Sentiment and NER).

I used models from Apache OpenNLP that can be found here and here. However, I could not find a model for analyzing text sentiment so I wrote code to train my model. I prepared CSV file with labeled data with the help of chatGPT. You can find it in the dataset directory.

Technology stack

• Kotlin
• Spring boot
• Apache OpenNLP
• Apache Common CSV
• Junit5

API

App has 2 endpoints

1. Text analysis

curl -X GET -H "Content-Type: application/json" -d '{ "text": "I am working at Apple company with my friend Jeff"}' localhost:8080/v1/analyze

The endpoint takes JSON as a request with a single field text with the text we want to analyze. It returns JSON with sentiment, person names, and organization names

Example response

{
    "sentiment": "NEUTRAL",
    "namedEntity": {
        "personNames": [
            "Jeff"
        ],
        "organizationNames": [
            "Apple"
        ]
    }
}

2. Sentiment model training

curl -X POST 'localhost:8080/v1/model/train-sentiment-model' -F 'file=@"dataset/labeled-sentiment-dataset.csv"' 

The endpoint takes a CSV file with text and sentiment columns. After successful upload, the app trains the model and saves it as .bin file in a model/sentiment/ directory that can be changed in the application.yaml.

To use a new model you have to update the application.yaml file with a path to a new sentimental model and restart the app. Later I might add an endpoint to change model without restarting app. It is just a simple demo app to see how OpenNLP library works

Example response

model/sentiment/en-sentiment-model-2024-03-20_22-06-33.bin

The sentiment model has around 84% accuracy. With more inputs, it could analyze text sentiment with better accuracy. Here are some example responses:

Input	Sentiment model response	Result
"My car broke and I can't get to the job"	"NEGATIVE"	✅
"This news is very sad"	"NEGATIVE"	✅
"Their products are affordable and have good quality"	"NEGATIVE"	❌
"The view from the top of the mountain was awesome"	"POSITIVE"	✅
"Another day, another dollar"	"NEUTRAL"	✅
"This new game is really amazing"	"POSITIVE"	✅
"I feel sad"	"NEUTRAL"	❌