TF-IDF

E-commerce websites, based on their structural ontology, provides access to a wide range of options and the ability to deal directly with manufacturers to receive cheaper products and services as well as receiving comments and ideas of the users on the provided products and services. This is a valuable source of information, which includes a large number of user reviews. It is difficult to check the bulk of the comments published manually and non-automatically. Hence, sentiment analysis is an automated and relatively new field of study, which extracts and analyzes people's attitudes and emotions from the context of the comments. The primary objective of this research is to analyze the content of users' comments on online sale e-commerce websites of handcraft products. Sentiment analysis techniques were used at sentence level and machine learning approach.  First, the pre-processing steps and TF-IDF method were implemented on the comments text. Next, the comments text were classified into two groups of products and services comments using Support Vector Machine (SVM) algorithm with 99.2% accuracy. Finally, the sentiment of comments was classified into three groups of positive, negative and neutral using XGBoost algorithm. The results showed, 95.23% and 95.12% accuracies for classification of sentiments in comments about products and services, respectively.

The study of surahs' structure has attracted researchers' attention in recent years. One of the theories herein is the theory of Topic Sameness which acknowledges that each surah of Quran has formed on a single topic. The theory of Introduction and Explanation as one of the most important branches of Topic Sameness, proposes that the Almighty states the topic of each surah at the first section, elaborates it at different parts of the surah in the forms such as stories, signals of nature, and future predictions, and concludes from the stated contents at the final part. In this paper, we accordingly intend to study the two theories using NLP techniques for the first time. In this regard, based on the three methods of tf-idf, word2vec and roots' accompaniment in verses, the similarity of Quranic roots is computed. Then, the amount of similarity of the concepts within surahs to each other is calculated and compared with the random mode. The results show that the studied surahs hold the inner coherence between the concepts so that they have been formed on a single topic or a few topics related to each other. In addition, the study on the similarity between the first and the body sections of each surah shows that the structure of Introduction and Explanation seems to be true for many surahs by the designed methodology. At the end, by comparing the similarity of surahs to each other versus their order distance in Quran and their revelation time distance, we realized that the whole Quran is also relatively organized in terms of surah' ordering.