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sentimental-analysis-using-nltk-and-transformers

You can download the data from here: https://drive.google.com/drive/folders/1sTCtiBLOdRqT5VPCrakVgFBtpJgcodFi?usp=sharing

Steps Involved in the Sentiment Analysis Project

  1. Importing Libraries: Imported necessary libraries like pandas, numpy, matplotlib.pyplot, seaborn, and nltk.
  2. Loading Data: Loaded the dataset using pandas and displayed the first few rows, filtering to 500 rows.
  3. Exploratory Data Analysis (EDA): Plotted the distribution of review scores using a bar chart and displayed an example text.
  4. Text Tokenization: Tokenized the example text into sentences and words using NLTK.
  5. Part-of-Speech Tagging and Named Entity Recognition: Tagged parts of speech and performed named entity recognition on the tokenized words.
  6. Downloading NLTK Resources: Downloaded NLTK resources like 'averaged_perceptron_tagger', 'maxent_ne_chunker', 'words', and 'vader_lexicon'.
  7. Sentiment Analysis with VADER: Used SentimentIntensityAnalyzer from NLTK to analyze example texts and the entire dataset, storing results in a dictionary and DataFrame.
  8. Visualization of Sentiment Scores: Visualized sentiment scores (compound, positive, neutral, negative) against review scores using bar charts.
  9. Using Hugging Face Transformers for Sentiment Analysis: Loaded the roberta-base model from transformers and calculated sentiment scores, storing results in a dictionary and DataFrame.
  10. Comparison of VADER and RoBERTa: Compared VADER and RoBERTa sentiment scores using pair plots and queried the dataset for specific sentiment examples.
  11. Using Hugging Face Pipeline for Sentiment Analysis: Used the Hugging Face sentiment analysis pipeline for quick sentiment analysis on sample texts.

Commands to Setup Project on Local Machine

  1. Clone the repository: 
    git clone https://github.com/dyavadi8769/sentimental-analysis-using-nltk-and-transformers.git
cd sentimental-analysis-using-nltk-and-transformers
  2. Create a virtual environment and activate it: 
    conda create -p env python==3.11.7 -y
conda activate env/
  3. Install the Required Dependecies: 
    pip install -r requirements.txt

yes!! Now you can start predicting 🙂

Author:

Author: Sai Kiran Reddy Dyavadi
Role  : Data Scientist
Email : dyavadi324@gmail.com