Peer Review #2 of "Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings (v0.1)"

Abstract

Sentiment analysis plays a key role in companies, especially stores, and increasing the accuracy in determining customers' opinions about products assists to maintain their competitive conditions. We intend to analyze the users' opinions on the website of the most immense online store in Iran; Digikala. However, the Persian language is unstructured which makes the pre-processing stage very difficult and it is the main problem of sentiment analysis in Persian. What exacerbates this problem is the lack of available libraries for Persian pre-processing, while most libraries focus on English. To tackle this, approximately 3 million reviews have been gathered in Persian from the Digikala website using web-mining techniques and have been used the fastText method to create a word embedding. It has been assumed it would dramatically cut down the need for the text pre-processing through the skip-gram method considering the position of the words in the sentence and the words' relations to each other. Another word embedding has been created using the TF-IDF in parallel with fastText to compare their performance.In addition, the results of the CNN, BiLSTM, Logistic Regression, and Naïve Bayes models have been compared. As a significant result, we obtained 0.996 AUC, and 0.956 F-score using fastText and CNN. In this article, not only it has been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is also better than other researches done in Persian. Avoiding complex text preprocessing is also important for other languages since most text preprocessing algorithms have been developed for English and cannot be used for other languages. The created word embedding due to its high accuracy and independence of pre-processing has other applications in Persian besides sentiment analysis.