Automatic Categorization of Documents Using Latent Semantic Analysis and Fuzzy Inference Algorithm of Mamdani

Based on the method of latent semantic analysis (LSA), a text matching algorithm was proposed in this paper for constructing an automated scoring system of Chinese test papers. Firstly, by fully considering the correlation between terms, texts of examinee's answers and standard answers were represented in lower-dimensional space and the model was improved using the way of singular value decomposition. Secondly, using LSA, the cosine similarity between the texts of examinee's answers and standard answers was taken as similarity criterion to determine the score of answer of each examination question. Experimental results show that by considering the semantic information of text, the proposed algorithm has satisfactory scoring results, and the work presented in this paper is a beneficial exploration for implementing semantic based automated scoring system.

The Qur'an, comprising over 80,000 words, 6,236 verses, and 114 surahs, presents a multifaceted and deeply significant text that demands a nuanced understanding of historical context, classical Arabic, and exegesis. To analyze and classify its content, various methodologies have been employed, including K-Nearest Neighbor (KNN) and Latent Semantic Analysis (LSA). This research investigates the effectiveness of combining KNN with LSA for multi-label topic classification of Qur'anic verses. The study reveals that KNN alone achieved a micro average F1-score of 0.49, demonstrating reliable performance particularly for topics such as "aqidah" (creed) and "worldly matters." When LSA was applied with 100 components, there was a decrease in performance, reflected by a drop in the micro average F1-score to 0.43 and an increase in Hamming loss to 0.1657. However, as the number of LSA components increased to 200 and 300, performance improved, with micro average F1-scores rising to 0.45 and 0.47, and Hamming loss values decreasing to 0.1507 and 0.1466, respectively. This indicates that while LSA can enhance KNN performance, optimal results are achieved with a higher number of components

Identifying dominant topics appearing in the Journal of Cleaner Production

Computing similarity of text-based assembly processes for knowledge retrieval and reuse

Recently, discovering learner behaviour has taken more attention in the field of e-learning. It aims to gain useful insights into the learning process of students despite the absence of direct interaction with teachers. In fact, the only available source of information in such environments is the log file that represents all possible interactions of learners with the e-learning system. This log file is characterised by the presence of noise, incomplete information, and a huge amount of data. In this article, a new approach based on learner trails analysis from the log file is proposed. It aims to discover the patterns of the real behaviour of learners and to determine their pedagogic orientations. The latent semantic analysis (LSA) method is used to extract the relationship between learners who have the same behaviour and to overcome the noise problem. The proposed approach has been validated using synthetic and genuine log files. The obtained results show the efficiency of the proposed method of discovering the behaviours of learners.

A quick and effective learning assessment is needed to evaluate the learning process. Many tools currently offer automatic assessment for subjective and objective questions; however, there is no such free tool that provides plagiarism detection among students for subjective questions in a Learning Management System (LMS). This research aims to create an automatic essay assessment in MoLearn LMS that can check students’ answers. At the same time, the LMS can also anticipate online cheating. This research employed two methods comprising (1) System Development Life Cycle (SDLC) waterfall model and (2) Latent Semantic Analysis (LSA) method. The former was used to design and create essay assessment applications, and the latter was employed to check the essay's answer and automatically detect students’ plagiarism. The result showed that MoLearn LMS was successfully working on detecting students’ plagiarism, leading to better LMS innovation. Automatic essay assessment accelerates grading time by about 8.04 times that of manual grading, which takes 5.52 seconds per question.

Common knowledge, also called intersubjectivity, is a core theme in the study of international cooperation and diplomacy. Yet International Relations (IR) lacks a method to systematically measure the degree of common knowledge. Drawing from research in computational linguistics, psychology, and communication, we introduce latent semantic analysis (LSA) to measure common knowledge in specific communicative exchanges between actors. We argue that the extent to which speaking partners use words in the same way and get in synch linguistically can be used to measure the degree of common knowledge, and this can be measured by the LSA method. We outline several ways LSA can be valuable to IR scholars and provide an empirical illustration of using this method in the case of Bretton Woods negotiations. The LSA method promises to help IR scholars seize the research opportunities offered by the digital age and build a bridge between qualitative and quantitative methods.

In today's digital era, social media data provides valuable insights into public opinion. This study implements the Graph Regularized Probabilistic Latent Semantic Analysis (GPLSA) method to analyze topics from social media data surrounding the 2024 Indonesian Presidential Election (Pemilu), as well as to evaluate the efficiency of the Probabilistic Latent Semantic Analysis (PLSA) algorithm. The research stages include collecting social media data on presidential debates and elections, text pre-processing, and applying the GPLSA method to identify main topics. The analysis results show that PLSA without graph achieved a topic coherence score of 0.653, indicating good consistency, while GPLSA decreased to 0.5, suggesting that the addition of graph regularization did not significantly enhance coherence. Additionally, PLSA without graph achieved a perplexity score of 12.138, indicating good predictive capability, while GPLSA increased to 12.511, showing that graph regularization did not improve the prediction of new words. PLSA without graph also produced topics relevant to election issues, while GPLSA generated topics influenced by graph regularization, though without significant improvement in topic quality. Sentiment analysis of social media posts provides insights into public responses to debates and election issues. Validation of the GPLSA model ensures relevant topic representation. This research contributes to the development of text analysis methods and offers valuable information for elections and democratic participation. These results can be utilized by stakeholders to make more strategic and informed decisions.

Online social media has become a major source of information gathering for a huge section of society. As the amount of information flows in online social media is enormous but on the other hand, the fact-checking sources are limited. This shortfall of fact-checking gives birth to the problem of misinformation and disinformation in the case of the truthfulness of facts on online social media which can have serious effects on the wellbeing of society. This problem of misconception becomes more rapid and critical when some events like the recent outbreak of Covid-19 happen when there is no or very little information is available anywhere. In this scenario, the identification of the content available online which is mostly propagated from person to person and not by any governing authority is very needed at the hour. To solve this problem, the information available online should be verified properly before being conceived by any individual. We propose a scheme to classify the online social media posts (Tweets) with the help of the BERT (Bidirectional Encoder Representations from Transformers)-based model. Also, we compared the performance of the proposed approach with the other machine learning techniques and other State of the art techniques available. The proposed model not only classifies the tweets as relevant or irrelevant, but also creates a set of topics by which one can identify a text as relevant or irrelevant to his/her need just by just matching the keywords of the topic. To accomplish this task, after the classification of the tweets, we apply a possible topic modelling approach based on latent semantic analysis and latent Dirichlet allocation methods to identify which of the topics are mostly propagated as false information.

Search engine technology was used to find information as needed easily, quickly and efficiently, including in searching the information about the hadith which was a second guideline of life for muslim besides the Holy Qur'an. This study was aim to build a specialized search engine to find information about a complete and eleven hadith in Indonesian language. In this research, search engines worked by using latent semantic analysis (LSA) and cosine similarity based on the keywords entered. The LSA and cosine similarity methods were used in forming structured representations of text data as well as calculating the similarity of the keyword text entered with hadith text data, so the hadith information was issued in accordance with what was searched. Based on the results of the test conducted 50 times, it indicated that the LSA and cosine similarity had a success rate in finding high hadith information with an average recall value was 87.83%, although from all information obtained level of precision hadith was found semantically not many, it was indicated by the average precision value was 36.25%.

The field of expert finding has seen a large number of approaches proposed both in universities and in industries, using a variety of new techniques in relevant data fields. This study tends to identify information retrieval method of latent semantic analysis and temporal graph for document retrieval. In this study, citation occurrence and author occurrence are independent variables and scales of expert author finding are dependent variables. The method used to evaluate judgment of document and author relevance in the test set formation phase is more similar to survey methods. Library method is used to study theoretical foundations and judge literature. This study has three populations: a) test set documents; b) people who make queries and judge relevance of retrieved documents; c) people who judge relevance of the retrieved experts. To measure judgments of document relevance, a method similar to peer tests is used. Among the retrieved results, repeated results are placed to determine accuracy and reliability of the judge. The degree of correlation obtained in this method is very high (0.98), indicating the reliability of the results. Regarding the results of the current study on application of latent semantic indexing (LSA) information retrieval model, which was ultimately used to retrieve expert authors, the performance of LSA-based retrieval model outperformed the baseline model. This was evident from the obtained metrics, including precision at the top 5 results (p@5) with a value of 0.895, mean average precision (MAP) of 0.839, and mean reciprocal rank (MRR) of 0.909. The improved retrieval performance can be attributed to the superior performance of the dimension reduction method compared to keyword matching.

An integrated approach for intrinsic plagiarism detection

n proposed work, we successfully implemented a news text summarization system using Natural Language Processing (NLP) techniques and the Latent Semantic Analysis (LSA) algorithm. The purpose of our project was to extract important information from a large volume of news articles and present it in a concise and easily understandable manner. To achieve this, we utilized the LSA algorithm, which is known for its ability to capture the underlying semantic structure of text. LSA employs a mathematical model to analyse relationships between words in a document, creating a semantic representation where words with similar contexts are grouped together in a vector space. The LSA-based summarization process involved several steps. First, we pre-processed the news articles by removing stop words, punctuation, and other non-relevant elements. Then, we constructed a term-document matrix, where rows represented words and columns represented documents, with matrix values representing word frequencies. Next, we applied Singular Value Decomposition (SVD) to the term-document matrix. SVD helped reduce the matrix's dimensionality by identifying the most important latent semantic concepts. This resulted in a lower-dimensional representation that captured the essential information. Finally, we identified the most important sentences in the news articles by measuring the cosine similarity between each sentence and the summary. Sentences with the highest cosine similarity scores were selected as summary sentences. The proposed system demonstrated the effectiveness of the LSA algorithm for news text summarization. By capturing the semantic structure of the text, it generated summaries that allowed users to understand the key points of a news article quickly and easily. Our implementation had practical applications for content recommendation systems, news aggregation platforms, and personalized news feeds. However, it is important to acknowledge the limitations of the LSA algorithm. It may struggle with handling idiomatic expressions and can be sensitive to the quality of the input data. These considerations highlight the need for ongoing research and development to enhance the performance and robustness of news text summarization systems.

This paper describes application of the basic methods of semantic analysis of text data – Porter stemming, frequency semantic analysis, latent semantic analysis and syntactic semantic analysis using an automated system. The system allows analyzing the text using these methods. The characteristics and features of the methods’ implementation as well as the obtained results of their applying to texts of small complexity are considered. The research allows to reveal features of usage of the methods according to the text analysis purposes.

In recent years, latent semantic analysis (LSA) has reached a level of maturity at which its presence is ubiquitous in technology as well as in simulation of cognitive processes. In spite of this, in recent years there has been a trend of subjecting LSA to some criticisms, usually because it is compared to other models in very specific tasks and conditions and sometimes without having good knowledge of what the semantic representation of LSA means, and without exploiting all the possibilities of which LSA is capable other than the cosine. This paper provides a critical review to clarify some of the misunderstandings regarding LSA and other space models. The historical stability of the predecessors of LSA, the representational structure of word meaning and the multiple topologies that could arise from a semantic space, the computation of similarity, the myth that LSA dimensions have no meaning, the computational and algorithm plausibility to account for meaning acquisition in LSA (in contrast to others models based on online mechanisms), the possibilities of spatial models to substantiate recent proposals, and, in general, the characteristics of classic vector models and their ease and flexibility to simulate some cognitive phenomena will be reviewed. The review highlights the similarity between LSA and other techniques and proposes using long LSA experiences in other models, especially in predicting models such as word2vec. In sum, it emphasizes the lessons that can be learned from comparing LSA-based models to other models, rather than making statements about "the best."