/LSTM-Sentiment-Analysis-Airline-Reviews-Text-Generation

Sentiment detection model using many-to-one LSTMs on airline text reviews and generate contextually relevant text by training on "Alice's Adventures in Wonderland".

Primary LanguageJupyter NotebookMIT LicenseMIT

Sentiment Analysis and Text Generation using Many-to-One LSTMs on Airline Reviews

Overview

This project explores sentiment analysis and text generation utilizing Long Short-Term Memory (LSTM) neural networks.

Sentiment Analysis

We use LSTMs to analyze airline sentiments, aiming to predict sentiment labels (0 or 1) based on customer reviews. Converting text reviews into numerical data, we utilize many-to-one LSTMs for accurate predictions.

Text Generation

Text generation is also explored by training LSTMs on "Alice's Adventures in Wonderland." The model learns to predict the next word in a sequence, creating coherent and contextually relevant sentences. Challenges like language variability and context dependence are addressed through entropy scaling and softmax temperature techniques.

Aim

This project's objectives are twofold:

  1. Develop a sentiment detection model using many-to-one LSTMs to predict sentiment labels (0 or 1) based on airline text reviews.
  2. Utilize many-to-one LSTMs for text generation, training on "Alice's Adventures in Wonderland" and predicting the next word in a sequence.

Data Description

  • airline_sentiment.csv: This dataset includes information on airline sentiments in CSV format, containing "airline_sentiment" (sentiment labels: 0 or 1) and "text" (customer reviews).

  • alice.txt: Project Gutenberg's eBook of "Alice’s Adventures in Wonderland" by Lewis Carroll, provided as a text file. This dataset serves as training data for text generation using many-to-one LSTMs.

Tech Stack

  • Language: Python
  • Libraries: pandas, numpy, keras, tensorflow, collections, nltk

Approach

Sentiment Analysis

Dataset

  • Obtain the airline sentiment dataset with sentiment labels and text reviews.

Preprocessing

  • Perform data preprocessing, including text cleaning, tokenization, and stop word removal.
  • Convert text reviews into a bag-of-words representation.

Many-to-One LSTM

  • Utilize many-to-one LSTM architecture for sentiment detection.
  • Feed the bag-of-words representation as input to the LSTM.

Training

  • Split the dataset into training and testing sets.
  • Train the LSTM model using the training set.
  • Evaluate model performance on the testing set.

Text Generation

Dataset

  • Obtain "Alice's Adventures in Wonderland" text dataset.

Preparing and Structuring

  • Preprocess the text data by cleaning, tokenizing, and structuring sentences and phrases.
  • Create sequences.

Many-to-One LSTM

  • Implement many-to-one LSTM architecture for text generation.
  • Train the LSTM model using the prepared dataset.

Challenges in Text Generation

  • Recognize challenges such as language variability and context dependence in text generation.
  • Understand that natural language uses diverse words with similar meanings, requiring careful consideration during generation.

Entropy Scaling

  • Explore entropy scaling to introduce controlled randomness into text generation.

Softmax Temperature

  • Introduce softmax temperature as a hyperparameter to control prediction randomness in LSTMs and neural networks.
  • Predict using temperature.

Modular Code Overview

  • data: Contains input data files.
  • lib: Includes a Jupyter Notebook with code and documentation.
  • output: Stores generated files and results.
  • Readme.md: Provides project information.
  • requirements.txt: Lists required packages.
  • Engine.py: The main engine file.
  • ML_Pipeline: Contains modules for different steps in the machine learning pipeline.