Trying to establish an Empirical finding upon taking a closer look on how different Machine Learning Models would work on Google-play-store-reviews dataset. Pretty simple binary calssification approach has been adopted. We are trying to identify if the reviews are "Energy-Related" or not.

• Naive Bayes
• Linear Classifier
• Support Vector Machine
• Extreme Gradient Boosting
• Sahallow Neural Networks
• Deep Neural Networks
  • Convolutional Neural Network (CNN)
  • Long Short Term Model (LSTM)
  • Gated Recurrent Unit (GRU)
  • Bidirectional (RNN)
  • Recurrent Convolutional Neural Network (RCNN)

• Count Vectors : Matrix notation of the dataset in which every row represents a document from the corpus, every column represents a term from the corpus, and every cell represents the frequency count of a particular term in a particular document.

• Word Level TF-IDF: Matrix representing tf-idf scores of every term in different documents

• N-gram Level TF-IDF: N-grams are the combination of N terms together. This Matrix representing tf-idf scores of N-grams

• Character Level TF-IDF : Matrix representing tf-idf scores of character level n-grams in the corpus

• Combinations Tried:

  • Count Vectors + Word Level TF-IDF
  • Count Vectors + N-gram Level TF-IDF
  • Count Vectors + Character Level TF-IDF
  • Word Level TF-IDF + N-gram Level TF-IDF
  • Word Level TF-IDF + Character Level TF-IDF
  • N-gram Level TF-IDF + Character Level TF-IDF
  • Count Vectors + Word Level TF-IDF + N-gram Level TF-IDF
  • Count Vectors + Word Level TF-IDF + Character Level TF-IDF
  • Count Vectors + N-gram Level TF-IDF + Character Level TF-IDF
  • Word Level TF-IDF + N-gram Level TF-IDF + Character Level TF-IDF
  • Count Vectors + Word Level TF-IDF + N-gram Level TF-IDF + Character Level TF-IDF

• Word Embeddings:

  Form of representing words and documents using a dense vector representation. The position of a word within the vector space is learned from text and is based on the words that surround the word when it is used.

  • Glove (wiki-news-300d-2M.vec)
  • FastText (wiki-news-300d-2M.vec)
  • Word2Vec (wiki-news-300d-2M.txt)

Details are in Final Report Sheet

https://www.analyticsvidhya.com

Ensemble Learning implementation is in plan