NLP Projects Repository

This repository contains various NLP projects developed by Himanshu Gangwar. Each project showcases different aspects of Natural Language Processing, including sentiment analysis, language translation, and speech emotion recognition.

Projects Overview

1. Movie Review Model

• Description: A sentiment analysis model trained on movie reviews. The goal is to classify whether a given movie review is positive or negative.
• Files:
  • Movie_review_model.ipynb
• Features:
  • Preprocessing of text data.
  • Model training using popular ML algorithms like Logistic Regression, Naive Bayes, or deep learning models.
  • Evaluation of model accuracy and performance.

2. Sentiment Analyse

• Description: A basic sentiment analysis model that predicts the sentiment (positive/negative) of textual input.
• Files:
  • Sentiment_analyse.ipynb
• Features:
  • Data preprocessing and vectorization techniques.
  • Uses popular NLP libraries such as NLTK or SpaCy.
  • Output visualization with metrics like confusion matrix.

3. Sentiment Analysis Project

• Description: An extended sentiment analysis project with a focus on refining the preprocessing steps and implementing deep learning models like LSTM or BERT for improved accuracy.
• Files:
  • Sentiment_analysis_project.ipynb
• Features:
  • Advanced preprocessing (lemmatization, stemming, stopword removal).
  • Implementation of RNN-based models and BERT for sentiment prediction.
  • Model evaluation and analysis.

4. Speech Emotions Model

• Description: A model that recognizes emotions from speech data. It classifies audio input into different emotional states like happy, sad, angry, etc.
• Files:
  • Speech_Emotions_Model.ipynb
• Features:
  • Audio data preprocessing (MFCC extraction).
  • Implementation using deep learning models (CNN, LSTM).
  • Evaluation using metrics like accuracy, precision, and recall.

5. End-to-Hindi Translation

• Description: A language translation model that translates English sentences to Hindi. The project uses sequence-to-sequence models or transformer models for translation tasks.
• Files:
  • end_to_hindi.ipynb
• Features:
  • Data preprocessing using tokenization.
  • Implementation using seq2seq models with attention mechanisms.
  • Performance evaluation using BLEU score.