AI-Sentiment-Classifier

Sentiment Analysis System Using LangChain and Ollama

Overview

This project implements a Sentiment Analysis System using the LangChain framework and Ollama models. The system classifies text inputs such as product reviews or social media posts into positive, negative, or neutral sentiments. It leverages a pre-trained Ollama model for text embedding and sentiment classification, integrated into a flexible LangChain pipeline.

Features

• Sentiment Classification: Classifies input text into positive, negative, or neutral categories.
• Ollama Integration: Uses Ollama’s pre-trained llama3.2 model for efficient sentiment analysis.
• LangChain: The LangChain framework manages the sentiment analysis workflow.

Requirements

• Python 3.8 or higher

• The following Python packages are required:

  pip install langchain ollama
  

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sentiment-analysis-langchain-ollama.git
   cd sentiment-analysis-langchain-ollama
   

2. Install the necessary dependencies:

   pip install -r requirements.txt
   

3. Ensure that you have access to the pre-trained Ollama model (llama3.2).

How to Use

1. Input: Provide a list of text inputs (e.g., reviews, comments) that you wish to analyze for sentiment.

2. Run the analysis: Execute the script main.py to analyze the sentiment of your input text:

   python main.py
   

3. Sample Output: The system will output the sentiment for each input text:

   Text: "The product was amazing and easy to use."
   Sentiment: Positive
   
   Text: "The experience was terrible. I do not recommend this."
   Sentiment: Negative
   
   Text: "The product is okay but could be improved."
   Sentiment: Neutral
   

Project Structure

├── main.py               # Main script for running sentiment analysis
├── README.md             # This README file
├── requirements.txt      # Python dependencies
└── sentiment_analysis.py # Contains the LangChain and Ollama implementation

Example Code

Here’s a simplified code snippet showing how to integrate LangChain with Ollama for sentiment analysis:

from langchain import PromptTemplate, LLMChain
from langchain.llms import Ollama

# Prompt for sentiment analysis
prompt_template = PromptTemplate(
    input_variables=["text"],
    template="Analyze the sentiment of the following text and classify it as positive, negative, or neutral:

{text}"
)

# Ollama model
llm = Ollama(model="llama3.2")

# LangChain setup
llm_chain = LLMChain(llm=llm, prompt=prompt_template)

# Sample input
texts = ["The product was fantastic!", "I had a bad experience.", "The service was decent."]

# Process and output results
for text in texts:
    result = llm_chain.run(text)
    print(f"Text: {text}
Sentiment: {result}
")

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Feel free to submit issues or pull requests. Contributions are welcome!

Contact

For any questions or feedback, reach out at info@tameronline.com.

This site is open source. Improve this page.