Textual analysis in the realm of business depends on text-processing techniques borrowed mainly from information retrieval. Yet, these text-processing techniques are not viable in text-based financial forecasting. In this paper, we suggest developing financial home-grown techniques for processing textual data, specifically in the course of scoring words where standard techniques are not appropriate in financial analysis. On that matter, we pursue two issues. First, we examine major information retrieval heuristics, where we find TF-IDF too facile not only in predicting trends but also in generating accurate results (in terms of errors) on large numbers in text-based financial analysis. Second, we work on a new heuristic satisfying financial concerns. We consider the relationship between the publication rate of information and its importance. The proposed heuristic provides results of unmatchable performance in both predicting trends and precision measures. In an additional analysis, we optimize our scheme using a genetic algorithm as an optimization technique and get greater precision. In comparison with TF-IDF, our proposed heuristic conduces to a 38. 5 percent lower error in closeness measures which is again reduced by 16. 46 percent with the help of a genetic algorithm. Our findings suggest that researchers in the field of financial textual analysis should not rely on standard information retrieval heuristics.

Background: Nowadays, due to the increased publication of articles in various scientific fields, identifying the publishing trends and emerging keywords in the texts of these articles is essential. Objectives: Thus, the present study identified and analyzed the keywords used in the published articles on medical librarianship and information. Methods: In the present investigation, an exploratory and descriptive approach was used to analyze librarianship and information articles published in specialized journals in this field from 1964 to 2019 by applying text mining techniques. The TF-IDF weighting algorithm was applied to identify the most important keywords used in the articles. The Python programming language was used to implement text mining algorithms. Results: The results obtained from the TF-IDF algorithm indicated that the words “, Library”, , “, Patient”, , and “, Inform”,with the weights of 95. 087, 65. 796, and 63. 386, respectively, were the most important keywords in the published articles on medical librarianship and information. Also, the words “, Catalog”, , “, Book”, , and “, Journal”,were the most important keywords used in the articles published between the years 1960 and 1970, and the words “, Patient”, , “, Bookstore”, , and “, Intervent”,were the most important keywords used in articles on medical librarianship and information published from 2015 to 2020. The words “, Blockchain”, , “, Telerehabilit”, , “, Instagram”, , “, WeChat”, , and “, Comic”,were new keywords observed in articles on medical librarianship and information between 2015 and 2020. Conclusions: The results of the present study revealed that the keywords used in articles on medical librarianship and information were not consistent over time and have undergone a change at different periods so that nowadays, this field of science has also changed following the needs of society with the advent and growth of information technologies.

This paper addresses automatic keyword extraction in Persian and English text documents. Generally, to extract keywords from a text, a weight is assigned to each token, and words characterized by higher weights are selected as the keywords. This study proposed four methods for weighting the words and compared these methods with  ve previous weighting techniques. The previous methods used in this paper include Term Frequency (TF), Term Frequency Inverse Document Frequency (TF-IDF), variance, Discriminative Feature Selection (DFS), and document length normalization based on unit words (LNU). The proposed weighting methods are presented using variance features and include variance to TF-IDF ratio, variance to TF ratio, the intersection of TF and variance, and the intersection of variance and IDF. For evaluation, the documents are clustered using the extracted keywords as feature vectors and by using K-means, Expectation Maximization (EM), and Ward hierarchical clustering methods. The entropy of the clusters and prede  ned classes of the documents are used as the evaluation metrics. For the evaluations, this study collected and labeled Persian documents. Results showed that the proposed weighting method, variance to TF ratio, showed the best performance for Persian texts. Moreover, the best entropy was found by variance to TD-IDF ratio for English texts.

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.

Fake information, better known as hoaxes, is often found on social media. Currently, social media is not only used to make friends or socialize with friends online, but some use it to spread hate speech and false information. Hoaxes are very dangerous in social life, especially in countries with large populations and ethnically diverse cultures, such as Indonesia. Although there have been many studies on detecting false information, the accuracy and efficiency still need to be improved. To help prevent the spread of these hoaxes, we built a model to identify false information in Indonesian using an ensemble classifier that combines the n-gram method, term frequency-inverse document frequency, and passive-aggressive classifier method. The evaluation process was carried out using 5000 samples from Twitter social media accounts in this study. The testing process is carried out using four schemes by dividing the dataset into training and test data based on the ratios of 90: 10, 80: 20, 70: 30, and 60: 40. The inspection results show that our software can accurately detect hoaxes at 91. 8%. We also found an increase in the accuracy and precision of hoax detection testing using the proposed method compared to several previous studies. The results show that our proposed method can be developed and used in detecting hoaxes in Indonesian on various social media platforms.