Natural language processing based advanced method of unnecessary video detection

In this study we have described the process of identifying unnecessary video using an advanced combined method of natural language processing and machine learning. The system also includes a framework that contains analytics databases and which helps to find statistical accuracy and can detect, accept or reject unnecessary and unethical video content. In our video detection system, we extract text data from video content in two steps, first from video to MPEG-1 audio layer 3 (MP3) and then from MP3 to WAV format. We have used the text part of natural language processing to analyze and prepare the data set. We use both Naive Bayes and logistic regression classification algorithms in this detection system to determine the best accuracy for our system. In our research, our video MP4 data has converted to plain text data using the python advance library function. This brief study discusses the identification of unauthorized, unsocial, unnecessary, unfinished, and malicious videos when using oral video record data. By analyzing our data sets through this advanced model, we can decide which videos should be accepted or rejected for the further actions

Multifunctional Product Marketing Using Social Media Based on the Variable-Scale Clustering

Customers\u27 demands have become more dynamic and complicated owing to the functional diversity and lifecycle reduction of products which pushes enterprises to identify the real-time needs of distinct customers in a superior way. Meanwhile, social media turned as an emerging channel where customers often spontaneously can express their perceptions and thoughts about products promptly. This paper examines the customer satisfaction identification and improvement problem based on social media mining. First, we proposed the public opinion sensitivity index (POSI) to uncover target customers from extensive short-textual reviews. Subsequently, we presented a customer segmentation approach based on the sentiment analysis and the variable-scale clustering (VSC). The approach is able to get several customer clusters with the same satisfaction level where customers belonging to each cluster have similar interests. Finally, customer-centered marketing strategies and customer difference marketing campaigns are planned under the shadow of customer segmentation results. The experiments illustrate that our proposed method can support marketing decision marketing in practice that enriches the intention of the current customer relationship management

Cancer patient perspectives during the COVID-19 pandemic: A thematic analysis of cancer blog posts

The content of online cancer patient blogs has previously been analyzed to inform physicians about the cancer experience and patient concerns. The coronavirus disease 2019 (COVID-19) pandemic has greatly affected cancer patients due to their vulnerable health status, as well as changes in cancer testing and treatment. We sought to qualitatively describe the concerns and experiences expressed online by cancer patients, survivors, and family members in relation to COVID-19. 152 blog posts written by cancer patients, survivors, or family members, were selected using combined Boolean searches and snowball sampling. Reviewers extracted subthemes from blog posts using line-by-line text analysis until a sufficient sample was achieved. Subthemes were hierarchically organized into major theme categories and illustrative quotations were identified. A total of 80 blog posts posted between January 20th and April 6th, 2020 were analyzed, revealing 23 subthemes. Major theme categories included: the direct and indirect impacts of COVID-19 on personal health and the health of others, comparisons between COVID-19 and the cancer experience, the impact of COVID-19 on social and psychological wellbeing, perspectives on government and the public response to COVID-19, and coping mechanisms and gratitude. COVID-19 has significantly affected cancer patients, survivors, and family members. Subthemes and quotations relating to perceived medical abandonment, patient mental health, and the impact of previous cancer trauma on the ability to cope with COVID-19 highlight the need for healthcare professionals to be cognizant of evolving patient concerns, so they may provide reassurance and appropriate care to their patients in these exceptional circumstances. Experience Framework This article is associated with the Patient, Family & Community Engagement lens of The Beryl Institute Experience Framework. (http://bit.ly/ExperienceFramework) Access other PXJ articles related to this lens. Access other resources related to this lens

Emotion Expression Extraction Method for Chinese Microblog Sentences

With the rapid spread of Chinese microblog, a large number of microblog topics are being generated in real-time. More and more users pay attention to emotion expressions of these opinionated sentences in different topics. It is challenging to label the emotion expressions of opinionated sentences manually. For this endeavor, an emotion expression extraction method is proposed to process millions of user-generated opinionated sentences automatically in this paper. Specifically, the proposed method mainly contains two tasks: emotion classification and opinion target extraction. We first use a lexicon-based emotion classification method to compute different emotion values in emotion label vectors of opinionated sentences. Then emotion label vectors of opinionated sentences are revised by an unsupervised emotion label propagation algorithm. After extracting candidate opinion targets of opinionated sentences, the opinion target extraction task is performed on a random walk-based ranking algorithm, which considers the connection between candidate opinion targets and the textual similarity between opinionated sentences, ranks candidate opinion targets of opinionated sentences. Experimental results demonstrate the effectiveness of algorithms in the proposed method

Cross Lingual Sentiment Analysis: A Clustering-Based Bee Colony Instance Selection and Target-Based Feature Weighting Approach

The lack of sentiment resources in poor resource languages poses challenges for the sentiment analysis in which machine learning is involved. Cross-lingual and semi-supervised learning approaches have been deployed to represent the most common ways that can overcome this issue. However, performance of the existing methods degrades due to the poor quality of translated resources, data sparseness and more specifically, language divergence. An integrated learning model that uses a semi-supervised and an ensembled model while utilizing the available sentiment resources to tackle language divergence related issues is proposed. Additionally, to reduce the impact of translation errors and handle instance selection problem, we propose a clustering-based bee-colony-sample selection method for the optimal selection of most distinguishing features representing the target data. To evaluate the proposed model, various experiments are conducted employing an English-Arabic cross-lingual data set. Simulations results demonstrate that the proposed model outperforms the baseline approaches in terms of classification performances. Furthermore, the statistical outcomes indicate the advantages of the proposed training data sampling and target-based feature selection to reduce the negative effect of translation errors. These results highlight the fact that the proposed approach achieves a performance that is close to in-language supervised models