ETSANet

This repository contains the code for our paper: Topic Sentiment Analysis Based on Deep Neural Network using Document Embedding Technique

Datasets

1. Topic detection dataset is : Pang and Lee movie review dataset B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts,” arXiv:cs/0409058, Jan. 2004, [Online]. Available: http://arxiv.org/abs/cs/0409058

2. Sentiment Classification Dataset includes:

   a) IMDB film review R. E., P. T. Pham, D. Huang, A. Y. Ng, and C. Potts, “Learning Word Vectors for Sentiment Analysis,” Proc. 49th Annu. Meet. Assoc. Comput. Linguist. Hum. Lang. Technol. - Vol. 1, pp. 142–150, 2007. [Online]. Available: https://dl.acm.org/citation.cfm?id=2002491

   b) Sentiment140 A. Go, R. Bhayani, and L. Huang, “Twitter Sentiment Classification using Distant Supervision,” Processing, vol., pp. 1–6, 2009. [Online]. Available: https://www-cs-faculty.stanford.edu/people/alecmgo/papers/TwitterDistantSupervision09.pdf

   Requirements

   1. Gensim

   2. pyLDAvis

   3. Spacy

   4. NLTK

   5. Niapy

   Description of files

   1. Preprocessing_Module.ipynb

      Preprocessing the datasets

   2. Module_LDA.ipynb

      Finding the best number of topics using Coherence Value, performing the LDA, and saving topics

   3. STRDF_Finding_Similar_Documents_using_Doc2vec.ipynb

      Making Doc2vec models, concatenating Doc2vec Models, and finding semantically topic-related documents correspondinng to the topics

   4. Sentiment_Classification_Hyperparameter_Optimization.ipynb

      Hyperparameter tuning (hyperparameters of CNN-GRU including Number of filters, Kernel size, Pool size, Number of GRU units) using GWO-WOA, comparing with other metaheuristic optimizers, and classifying the Sentiments of documents using the CNN-GRU

   5. Classification_using_Other_Classifiers.ipynb

   Classifying semantically topic-related documents using different classifiers