/SentimentAnalysis

Sentiment Analysis of Kindle Reviews

Primary LanguageJupyter Notebook

Sentiment Analysis

This project analyses the performance of different methods in the challenging task of sentiment analysis. In order to do so, Amazon Kindle data was obtained from (https://nijianmo.github.io/amazon/index.html).

Implementation

The implementation process consisted in four main steps:

  • Pre-processing: Sampling (to overcome the class imbalance issue) and text cleaning.
  • Exploratory Data Analysis (EDA): N-grams, word count, etc.
  • Classification: Used both classical machine learning methods (such as Gradient Boosting, SVMs and Gaussian Naive Bayes) and deep learning methods (e.g. BERT, RoBERTa, LSTMs)
  • Text generation

Enviroment

  • Python 3.6+
  • Pandas
  • Spacy
  • Tqdm
  • Spacymoji
  • Numpy
  • Sklearn
  • Imblearn
  • Matplotlib
  • Seaborn
  • Gensim
  • PyLDAvis
  • Logging
  • Nltk
  • Wordcloud
  • Torchbearer
  • PyTorch
  • Transformers

Install all python modules with

pip install -r requirements.txt

or if you have different versions of Python installed:

pip3 install -r requirements.txt

Authors