Persian sentiment analysis of an online store independent of pre-processing using convolutional neural network with fastText embeddings
| Autor: | Yanhui Guo, Sajjad Shumaly, Mohsen Yazdinejad |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Word embedding
General Computer Science Text mining Computer science Data Mining and Machine Learning Convolutional neural network 02 engineering and technology computer.software_genre lcsh:QA75.5-76.95 Skip gram World Wide Web and Web Science Naive Bayes classifier Sentiment analysis Artificial Intelligence 020204 information systems FastText 0202 electrical engineering electronic engineering information engineering Preprocessor Persian Web mining business.industry Natural language processing Data Science language.human_language Natural Language and Speech language 020201 artificial intelligence & image processing Artificial intelligence lcsh:Electronic computers. Computer science Web scrapping business computer Sentence Pseudo labeling |
| Zdroj: | PeerJ Computer Science PeerJ Computer Science, Vol 7, p e422 (2021) |
| ISSN: | 2376-5992 |
| Popis: | 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 were gathered in Persian from the Digikala website using web-mining techniques, and the fastText method was used to create a word embedding. It was assumed that this would dramatically cut down on the need for 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 Convolutional Neural Network (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 has it been demonstrated to what extent it is possible to be independent of pre-processing but also the accuracy obtained is 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. |
| Databáze: | OpenAIRE |
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