Microblog Dimensionality Reduction—A Deep Learning Approach

To derive latent topics from free-text responses on the negative impact of the pandemic on research activities and determine similarities and differences in the resulting themes between academic-based and clinical-based researchers. We performed a secondary analysis of free-text responses from a cross-sectional online survey conducted by the Japan Academy of Nursing Science of its members in early 2020. The participants were categorized into two groups by workplace (academic-based and clinical-based researchers). Latent Dirichlet allocation (LDA) topic modeling was used to extract latent topics statistically and list important keywords/text associated with the topics. After organizing similar topics by principal component analysis (PCA), we finally derived topic-associated themes by reading the keywords/texts and determining the similarity and differences of the themes between the two groups. A total of 201 respondents (163 academic-based and 38 clinical-based researchers) provided free-text responses. LDA identified eight and three latent topics for the academic-based and clinical-based researchers, respectively. While PCA re-grouped the eight topics derived from the former group into four themes, no merging of the topics from the latter group was performed resulting in three themes. The only theme common to the two groups was "barriers to conducting research," with the remaining themes differing between the groups. Using LDA topic modeling with PCA, we identified similarities and differences in the themes described in free-text responses about the negative impact of the pandemic between academic-based and clinical-based researchers. Measures to mitigate the negative impact of pandemics on nursing research may need to be tailored separately.

Despite the unique characteristics of urban forests, the motivating factors of urban forest visitors have not been clearly differentiated from other types of the forest resource. This study aims to identify the motivating factors of urban forest visitors, using latent Dirichlet allocation (LDA) topic modeling based on social big data. A total of 57,449 cases of social text data from social blogs containing the keyword “urban forest” were collected from Naver and Daum, the major search engines in South Korea. Then, 17,229 cases were excluded using morpheme analysis and stop word elimination; 40,110 cases were analyzed to identify the motivating factors of urban forest visitors through LDA topic modeling. Seven motivating factors—“Cafe-related Walk”, “Healing Trip”, “Daily Leisure”, “Family Trip”, “Wonderful View”, “Clean Space”, and “Exhibition and Photography”—were extracted; each contained five keywords. This study elucidates the role of forests as a place for healing, leisure, and daily exercise. The results suggest that efforts should be made toward developing various programs regarding the basic functionality of urban forests as a natural resource and a unique place to support a diversity of leisure and cultural activities.

This paper presents research of an application of a latent semantic analysis (LSA) model for the automatic evaluation of short answers (25 to 70 words) to open-ended questions. In order to reach a viable application of this LSA model, the research goals were as follows: (1) to develop robustness, (2) to increase accuracy, and (3) to widen portability. The methods consisted of the following tasks: firstly, the implementation of word bigrams; secondly, the implementation of combined models of unigrams and bigrams using multiple linear regression; and, finally, the addition of an adjustment step after the score attribution taking into consideration the average of the words of the answers. The corpus was composed by 359 answers produced according to two questions from a Brazilian public university’s entrance examination, which were previously scored by human evaluators. The results demonstrate that the experiments produced accuracy about 84.94 %, while the accuracy of the two human evaluators was about 84.93 %. In conclusion, it can be seen that the automatic evaluation technology shows that it is reaching a high level of efficiency.

Integrating patent analysis into technology roadmapping: A latent dirichlet allocation based technology assessment and roadmapping in the field of Blockchain

BackgroundHeart disease continues to be the leading cause of death in men and women in the United States. The COVID-19 pandemic has further led to increases in various long-term cardiovascular complications.ObjectiveThis study analyzed public conversations related to heart disease and heart health on Facebook in terms of their thematic topics and sentiments. In addition, it provided in-depth analyses of 2 subtopics with important practical implications: heart health for women and heart health during the COVID-19 pandemic.MethodsWe collected 34,885 posts and 51,835 comments spanning from June 2016 to June 2021 that were related to heart disease and health from public Facebook pages and groups. We used latent Dirichlet allocation topic modeling to extract discussion topics illuminating the public’s interests and concerns regarding heart disease and heart health. We also used Linguistic Inquiry and Word Count (Pennebaker Conglomerates, Inc) to identify public sentiments regarding heart health.ResultsWe observed an increase in discussions related to heart health on Facebook. Posts and comments increased from 3102 and 3632 in 2016 to 8550 (176% increase) and 14,617 (302% increase) in 2021, respectively. Overall, 35.37% (12,340/34,885) of the posts were created after January 2020, the start of the COVID-19 pandemic. In total, 39.21% (13,677/34,885) of the posts were by nonprofit health organizations. We identified 6 topics in the posts (heart health promotion, personal experiences, risk-reduction education, heart health promotion for women, educational information, and physicians’ live discussion sessions). We identified 6 topics in the comments (personal experiences, survivor stories, risk reduction, religion, medical questions, and appreciation of physicians and information on heart health). During the pandemic (from January 2020 to June 2021), risk reduction was a major topic in both posts and comments. Unverified information on alternative treatments and promotional content was also prevalent. Among all posts, 14.91% (5200/34,885) were specifically about heart health for women centering on local event promotion and distinctive symptoms of heart diseases for women.ConclusionsOur results tracked the public’s ongoing discussions on heart disease and heart health on one prominent social media platform, Facebook. The public’s discussions and information sharing on heart health increased over time, especially since the start of the COVID-19 pandemic. Various levels of health organizations on Facebook actively promoted heart health information and engaged a large number of users. Facebook presents opportunities for more targeted heart health interventions that can reach and engage diverse populations.

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."

Multi-faceted book search engine presents diverse category-style options to allow users to refine search results without re-entering a query. In this paper, we propose a novel multi-faceted book search engine that utilizes users' query-related latent intents mined from click-through logs as multiple facets for books. The latent query intents can be effectively and efficiently discovered by applying the Sparse Latent Semantic Analysis (LSA) model to users' query and clicking behaviors in the click-through logs. This paper presents the details to improve the multi-faceted book search by incorporating the compact representation of query-intent-book relationships generated by Sparse LSA into the off-line and online processing procedures. The specificity of latent query intents can be flexibly changed by adjusting the sparsity level of projection matrix in the Sparse LSA model. We evaluated our approach on CADAL click-through logs containing 45,892 queries and 164,822 books. The experimental results show the Sparse LSA model with more sparse projection matrix tends to discover the more specific latent query intents. The latent query intents suggested by our approach usually gain the high user satisfaction ratio.

Dimensionality reduction is one of the most important tasks in improving hyperspectral image (HSI) classification performance and has been widely studied. In traditional dimensionality reduction methods, the hyperspectral data cube is first converted to a matrix, and then, the matrix-algebra is employed, in which the spatial structure is usually not taken into consideration. To jointly utilize spectral and spatial information, researches considering HSI as a tensor have attracted more and more attention. In this letter, we propose a group-based tensor model for HIS dimensionality reduction. The local and nonlocal spatial information of HSI cubes is explored by segmenting the original HSI tensors into a lot of small tensors and grouping them into clusters. Finally, the clusters are projected into low-rank subspace to obtain a proper feature space. Experimental results on two real hyperspectral data sets exhibit the effectiveness of the proposed algorithm for HSI-dimensionality reduction.

Traditional clustering methods are not very effective when dealing with high-dimensional and huge datasets. Even if there are some traditional dimensionality reduction methods such as Principal components analysis (PCA), Linear discriminant analysis (LDA) and T-distributed stochastic neighbor embedding (T-SNE), they still can not significantly improve the effect of the clustering algorithm in this scenario. Recent studies have combined Non-linear dimensionality reduction achieved by deep neural networks with hard-partition clustering, and have achieved reliability results, but these methods can not update the parameters of dimensionality reduction and clustering at the same time. We found that soft-partition clustering can be well combined with deep embedding, and the membership of Fuzzy c-means (FCM) can solve the problem that gradient descent can not be implemented because the assignment process of the hard-partition clustering algorithm is discrete, so that the algorithm can update the parameters of deep neural network (DNN) and cluster centroids at the same time. We build an continuous objective function that combine the soft-partition clustering with deep embedding, so that the learning representations can be cluster-friendly. The experimental results show that our proposed method of simultaneously optimizing the parameters of deep dimensionality reduction and clustering is better than the method with separate optimization.

Latent semantic analysis (LSA) is a generalized vector space method that uses dimension reduction to generate term correlations for use during the information retrieval process. We hypothesized that even though the dimension reduction establishes correlations between terms, the dimension reduction is causing a degradation in the correlation of a term to itself (self-correlation). In this article, we have proven that there is a direct relationship to the size of the LSA dimension reduction and the LSA self-correlation. We have also shown that by altering the LSA term self-correlations we gain a substantial increase in precision, while also reducing the computation required during the information retrieval process.

When people search for documents, they eventually want content, not words. Hence, search engines should relate documents more by their underlying concepts than by the words they contain. One promising technique to do so is Latent Semantic Indexing (LSI). LSI dramatically reduces the dimension of the document space by mapping it into a space spanned by conceptual indices. Empirically, the number of concepts that can represent the documents are far fewer than the great variety of words in the textual representation. Although this almost obviates the problem of lexical matching, the mapping incurs a high computational cost compared to document parsing, indexing, query matching, and updating. This article accomplishes several things. First, it shows how the technique underlying LSI is just one example of a unitary operator, for which there are computationally more attractive alternatives. Second, it proposes the Haar transform as such an alternative, as it is memory efficient, and can be computed in linear to sublinear time. Third, it generalizes LSI by a multiresolution representation of the document space. The approach not only preserves the advantages of LSI at drastically reduced computational costs, it also opens a spectrum of possibilities for new research.

Web transaction data between web visitors and web functionalities usually convey users’ task-oriented behavior patterns. Clustering web transactions, thus, may capture such informative knowledge, in turn, build user profiles, which are associated with different navigational patterns. For some advanced web applications, such as web recommendation or personalization, the aforementioned work is crucial to make web users get their preferred information accurately. On the other hand, the conventional web usage mining techniques for clustering web objects often perform clustering on usage data directly rather than take the underlying semantic relationships among the web objects into account. Latent Semantic Analysis (LSA) model is a commonly used approach for capturing semantic associations among co-occurrence observations.. In this paper, we propose a LSA-based approach for such purpose. We demonstrated usability and scalability of the proposed approach through performing experiments on two real world datasets. The experimental results have validated the method’s effectiveness in comparison with some previous studies.KeywordsReal World DatasetUser SessionVery Large Data BaseUser TransactionSingle Value DecompositionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

While online peer review’s impact on EFL argumentative writing has been widely studied, its role in shaping narrative writing remains underexplored. This study investigated changes in cohesion and coherence in EFL learners’ narrative writing following online peer review. Thirty first-year English majors in China completed a two-round narrative writing task on the Peerceptiv platform. Subjective peer ratings and objective Coh-Metrix indices were analyzed. Due to non-normal data distribution, bootstrapped paired-samples t -tests with 10,000 resamples were employed to compare writing drafts. Results revealed selective changes in cohesion and coherence indices. Subjective Peerceptiv ratings showed significant gains in Topic Sentence and Organization (cohesion), and in Emotion and Concluding Part (coherence), suggesting improvements in local and meaning-focused aspects in narrative writing. However, Focus and Transition remained unchanged, indicating EFL learners’ difficulty with text-level control. Objective Coh-Metrix analysis revealed increased use of overall (CNCALL) and causal connectives (CNCCaus), and decreased adjacent sentence stem overlap (CRFSO1) under cohesion indices, suggesting more lexical variety. Only causal cohesion (SMCAUSr) improved among coherence indices; Latent Semantic Analysis (LSA) and other situation model measures remained stable, reflecting limited influence on deeper semantic integration. These findings suggest that peer review may support cohesion and coherence development in EFL narrative writing, especially when supported by rubrics that balance local and global features. Given the limited change in deeper semantic coherence and the study’s exploratory design, future research with extended interventions and comparison groups is recommended.

This paper introduces a novel, in-depth approach of analyzing the differences in writing style between two famous Romanian orators, based on automated textual complexity indices for Romanian language. The considered authors are: (a) Mihai Eminescu, Romania’s national poet and a remarkable journalist of his time, and (b) Ion C. Bratianu, one of the most important Romanian politicians from the middle of the 18th century. Both orators have a common journalistic interest consisting in their desire to spread the word about political issues in Romania via the printing press, the most important public voice at that time. In addition, both authors exhibit writing style particularities, and our aim is to explore these differences through our ReaderBench framework that computes a wide range of lexical and semantic textual complexity indices for Romanian and other languages. The used corpus contains two collections of speeches for each orator that cover the period 1857–1880. The results of this study highlight the lexical and cohesive textual complexity indices that reflect very well the differences in writing style, measures relying on Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) semantic models.

Text representation, which is a fundamental and necessary process for text-based intelligent information processing, includes the tasks of determining the index terms for documents and producing the numeric vectors corresponding to the documents. In this paper, multi-word, which is regarded as containing more contextual semantics than individual word and possessing the favorable statistical characteristics, is proposed as an alternative index terms in vector space model for text representation with theoretical support. We investigate the traditional indexing methods as TF*IDF (term frequency inverse document frequency) and LSI (latent semantic indexing) for comparative study. The performances of TF*IDF, LSI and multi-word are examined on the tasks of text classification, which includes information retrieval (IR) and text categorization (TC), in Chinese and English document collection respectively. We also attempt to tune the rescaling factor of LSI and observe its effectiveness in text classification. The experimental results demonstrate that TF*IDF and multi-word are comparable when they are used for IR and TC and LSI is the poorest one of them. Moreover, the rescaling factor of LSI has an insignificant influence on its effectiveness on text classification for both Chinese and English text classification.