Sentiment Analysis Chatbot

Text Generation and Sentiment Analysis Application

Overview

• Utilized Google LLM Modal Gemini-Pro for text generation, providing superior response compared to smaller models available in Hugging Face.
• Employed Hugging Face Modal cardiffnlp/twitter-roberta-base-sentiment-latest for text classification and sentiment prediction.
• Developed a Streamlit web page to display the classification results.
• Deployed the model on a Streamlit website due to the expiration of AWS free tier.

Scope of Improvement

• Deployment:

  • Consider deploying the application on AWS EC2 VM.
  • Implement Docker to build the container and utilize AWS ECR to store the private Docker image.
  • Integrate GitHub Actions for continuous integration and continuous deployment (CI/CD) pipeline.

• Model Enhancement:

  • Improve text classification model accuracy by fine-tuning on a custom dataset.
  • Utilize advanced prompt templates for the Gemini-Pro model to achieve desired outputs.

Future Work

• Connect all components to develop an industry-grade application.
• Explore further optimizations and enhancements to the models and deployment process.

Sentiment Analysis Report

Date: 21-04-2024

Dataset Overview:

• Total number of texts/documents analyzed: 54

Sentiment Distribution:

• Positive Sentiments: 72.22%
• Negative Sentiments: 12.96%
• Neutral Sentiments: 14.81%

Sentiments Examples:

1. Positive Sentiments:

   • With resilience and a positive mindset, I embrace setbacks as opportunities for growth and learning.
   • Yes, I thrive on exploring the unfamiliar and broadening my horizons.
   • Friendship is a precious gift that enriches our lives with love, support, and

2. Negative Sentiments:

   • Yes, sadness is an emotion I have experienced.
   • Life's burdens weigh heavy upon my weary soul.
   • Disappointment is like a dark cloud, covering the sun of my expectations.

3. Neutral Sentiments:

   • Bad news can be upsetting, but it's important to remember that it can also be an opportunity for growth and learning.
   • Forgiveness is not forgetting, but it is choosing to let go of the hurt and anger that binds us.
   • With a mixture of excitement, curiosity, and perhaps a hint of anxiety.

Key Findings:

• According to the model's predictions, approximately 72% of sentiments are positive
• neutral and negative sentiments account for roughly 14.8% and 13% respectively.

Recommendations:

• Due to the small dataset size and the tendency of the LLM model to generate positive sentiment statements for various questions, it's crucial to enhance result accuracy.
• Implement fine-tuning of a classification model using our dataset to address this challenge effectively.
• Fine-tuning the model can significantly improve accuracy, leading to more reliable outcomes.

Conclusion:

In conclusion, the analysis underscores the importance of addressing the limitations posed by the small dataset and the predisposition of the LLM model towards positive sentiment responses. By leveraging fine-tuning techniques with a classification model, there exists a promising avenue to substantially enhance result accuracy. This proactive approach not only mitigates potential biases but also ensures more dependable and precise outcomes in sentiment analysis tasks.

Steps to Install the Sentiment Analysis Chatbot Locally

1. Store Google LLM API Key:

   • Store your Google LLM API key in the local environment with the name GOOGLE_API_KEY.

2. Create Virtual Environment:

   • Create a virtual environment using conda:

     conda create -p venv python=3.9.19

3. Activate the Virtual Environment:

   • Activate the created environment using conda:

     conda activate ./venv

4. Install Required Python Libraries:

   • Install the required Python libraries listed in requirements.txt using pip:

     pip install -r requirements.txt

5. Run the Chatbot Application:

   • Execute the following command in the terminal to run the chatbot application:

     streamlit run qachat.py

Once these steps are completed, you should have the sentiment analysis chatbot up and running locally on your machine.