scholarly journals A New Approach for Video Concept Detection Based on User Comments

2021 ◽  
Vol 20 (No.4) ◽  
pp. 629-649
Author(s):  
Maha Thabet ◽  
Mehdi Ellouze ◽  
Mourad Zaied
Keyword(s):  
Language Processing ◽  
Research Effort ◽  
Concept Detection ◽  
New Approach ◽  
User Comments ◽  
Semantic Concepts ◽  
Youtube Videos ◽  
High Level ◽  
Video Concept Detection ◽  
Processing Techniques

Video concept detection means describing a video with semantic concepts that correspond to the content of the video. The concepts help to retrieve video quickly. These semantic concepts describe high-level elements that depict the key information present in the content. In recent years, many efforts have been done to automate this task because the manual solution is time-consuming. Nowadays, videos come with comments. Therefore, in addition to the content of the videos, the comments should be analyzed because they contain valuable data that help to retrieve videos. This paper focused especially on videos shared on social media. The specificity of these videos was the presence of massive comments. This paper attempted to exploit comments by extracting concepts from them. This would support the research effort that works only on the visual content. Natural language processing techniques were used to analyze comments and to filter words to retain only the ones that could be considered as concepts. The proposed approach was tested on YouTube videos. The results demonstrated that the proposed approach was able to extract accurate data and concepts from the comments that could be used to ease the retrieval of videos. The findings supported the research effort of working on the visual and audio contents of the videos.

scholarly journals Let’s play on Facebook: using sentiment analysis and social media metrics to measure the success of YouTube gamers’ post types

2019 ◽  
Author(s):  
Flora Poecze ◽  
Claus Ebster ◽  
Christine Strauss
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Nearest Neighbor ◽  
Consumer Feedback ◽  
Youtube Videos ◽  
The Masses ◽  
Processing Techniques ◽  
Facebook Pages ◽  
Future Work

AbstractThis paper discusses the analysis results of successful self-marketing techniques on Facebook pages in the cases of three YouTube gamers: PewDiePie, Markiplier, and Kwebbelkop. The research focus was to identify significant differences in terms of the gamers’ user-generated Facebook metrics and commentary sentiments. Analysis of variance (ANOVA) and k-nearest neighbor sentiment analysis were employed as core research methods. ANOVA of the classified post categories revealed that photos tended to show significantly more user-generated interactions than other post types, while, on the other hand, re-posted YouTube videos gained significantly fewer numbers in the retrieved metrics than other content types. K-nearest neighbor sentiment analysis pointed out underlying follower negativity in cases where user-generated activity was relatively low, thereby improving the understanding of the opinion of the masses previously hidden behind metrics such as the number of likes, comments, and shares. The paper at hand highlights the methodological design of the study as well as a detailed discussion of key findings and their implications, and future work. The results per se indicate the need to utilize natural language processing techniques to optimize brand communication on social media and highlight the importance of considering machine learning sentiment analysis techniques for a better understanding of consumer feedback.

scholarly journals A New Approach for Semi-Automatic Building and Extending a Multilingual Terminology Thesaurus

2019 ◽  
Vol 28 (02) ◽  
pp. 1950008
Author(s):  
Aleš Horák ◽  
Vít Baisa ◽  
Adam Rambousek ◽  
Vít Suchomel
Keyword(s):  
Language Processing ◽  
Real Life ◽  
The Other ◽  
Automatic Extraction ◽  
Web Based ◽  
New Approach ◽  
Land Surveying ◽  
Processing Techniques ◽  
Current Terminology ◽  
New System

This paper describes a new system for semi-automatically building, extending and managing a terminological thesaurus — a multilingual terminology dictionary enriched with relationships between the terms themselves to form a thesaurus. The system allows to radically enhance the workow of current terminology expert groups, where most of the editing decisions still come from introspection. The presented system supplements the lexicographic process with natural language processing techniques, which are seamlessly integrated to the thesaurus editing environment. The system’s methodology and the resulting thesaurus are closely connected to new domain corpora in the six languages involved. They are used for term usage examples as well as for the automatic extraction of new candidate terms. The terminological thesaurus is now accessible via a web-based application, which (a) presents rich detailed information on each term, (b) visualizes term relations, and (c) displays real-life usage examples of the term in the domain-related documents and in the context-based similar terms. Furthermore, the specialized corpora are used to detect candidate translations of terms from the central language (Czech) to the other languages (English, French, German, Russian and Slovak) as well as to detect broader Czech terms, which help to place new terms in the actual thesaurus hierarchy. This project has been realized as a terminological thesaurus of land surveying, but the presented tools and methodology are reusable for other terminology domains.

scholarly journals Approach for social media content-based analysis for vacation resorts

2019 ◽  
Vol 15 (3) ◽  
Author(s):  
Snezhana Sulova ◽  
Boris Bankov
Keyword(s):  
Social Media ◽  
Language Processing ◽  
Text Processing ◽  
Automated Analysis ◽  
New Approach ◽  
Useful Knowledge ◽  
Media Messages ◽  
Customer Feedback ◽  
Processing Techniques ◽  
The Impact

The impact of social networks on our liveskeeps increasing because they provide content,generated and controlled by users, that is constantly evolving. They aid us in spreading news, statements, ideas and comments very quickly. Social platforms are currently one of the richest sources of customer feedback on a variety of topics. A topic that is frequently discussed is the resort and holiday villages and the tourist services offered there. Customer comments are valuable to both travel planners and tour operators. The accumulation of opinions in the web space is a prerequisite for using and applying appropriate tools for their computer processing and for extracting useful knowledge from them. While working with unstructured data, such as social media messages, there isn’t a universal text processing algorithm because each social network and its resources have their own characteristics. In this article, we propose a new approach for an automated analysis of a static set of historical data of user messages about holiday and vacation resorts, published on Twitter. The approach is based on natural language processing techniques and the application of machine learning methods. The experiments are conducted using softwareproduct RapidMiner. 

scholarly journals Usense: A User Sentiment Analysis Model for Movie Reviews by Applying LSTM

2020 ◽  
pp. 369-372
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Linear Kernel ◽  
Analysis Model ◽  
Large Dataset ◽  
Major Drawback ◽  
User Comments ◽  
Processing Techniques ◽  
Better Than

Sentiment analysis is a field which deals with assessing the sentiments or emotions of the users on products and services. It takes user comments as input and applies natural language processing techniques to identify the mood of the user. Usually a sentiment is deemed to be positive, negative or neutral depending upon the mood that he expresses in the comments or feedbacks. It is largely used by businesses to improve products and services and also to present its customers with a set of products and services based on their likes and dislikes. State-of-the-art indicates many techniques have been applied in past such as, linear regression and SVM models. Recurrent Neural Networks (RNNs) have improved the way in which sentiment analysis could be done with greater accuracy, but they suffer from major drawback when applied to longer sentences. This paper proposes a sentiment analysis model using Long ShortTerm Memory (LSTM) based approach , which is a variant of RNNs. LSTMs are good in handling long sentence data. The model is applied to reviews collected from IMDB dataset. It is large dataset that contains 50K reviews. Out of the available reviews 50 % are used for training purpose and 50% are used for testing purpose. The model gives a training accuracy of 92% and validation accuracy of 85% which is neither an over fit nor an under fit. The overall accuracy here is 85%, which seems to be better than some of the existing techniques such as SVM with linear kernel.

Mid-Level Image Descriptors

2013 ◽  
pp. 46-60
Author(s):  
Jean Martinet ◽  
Ismail Elsayad
Keyword(s):  
Text Processing ◽  
Point Of View ◽  
Intermediate Step ◽  
Low Level ◽  
Image Descriptors ◽  
Semantic Concepts ◽  
Standard Text ◽  
A Chain ◽  
High Level ◽  
Processing Techniques

We present in this chapter a classification of image descriptors, from the low level to the high level, introducing the notion of intermediate level. This level denotes a representation level lying between low-level features – such as color histograms, texture or shape descriptors, and high-level features – semantic concepts. In a chain of process point of view, mid-level descriptors represent an intermediate step or stage between low and high level, dedicated to specific tasks such as annotation, object detection/recognition, or similarity matching. After introducing a definition for the three different levels, we review a number of approaches making use of such intermediate levels. We namely focus on different approaches making an analogy with text processing, by adapting and applying standard text processing techniques to image indexing.

scholarly journals Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

2021 ◽  
Vol 11 (2) ◽  
pp. 6845-6848
Author(s):  
W. M. S. Yafooz ◽  
E. A. Hizam ◽  
W. A. Alromema
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Social Issues ◽  
Ensemble Methods ◽  
Support Vector ◽  
Khat Chewing ◽  
User Comments ◽  
Arabic Sentiment Analysis ◽  
Processing Techniques

Sentiment analysis plays an important role in obtaining speakers' opinions or feelings towards events, products, topics, or services, helping businesses to improve their products. Moreover, governments and organizations investigate and solve current social issues by analyzing perspectives and feelings. This study evaluated the habit of chewing Khat (qat) leaves among the Yemeni society. Chewing Khat plant leaves, is a common habit in Yemen and East Africa. This paper proposes a model to detect information about the Khat chewing habit, how people explore it, and the preference for Khat leaves among Arabic people. A dataset consisting of user comments on 18 youtube videos was prepared through several natural language processing techniques. Several experiments were conducted using six machine learning classifiers and four ensemble methods. Support Vector Machine and Linear Regression had almost 80% accuracy, whereas xgboot was the most accurate ensemble method reaching 77%.

Language and Aging

2019 ◽  
pp. 294-316
Author(s):  
Jonathan E. Peelle
Keyword(s):  
Language Processing ◽  
Brain Function ◽  
Neural Systems ◽  
Language Abilities ◽  
Physiological Mechanisms ◽  
Cognitive Changes ◽  
Older Adulthood ◽  
Neural Organization ◽  
Age Related ◽  
High Level

Language processing in older adulthood is a model of balance between preservation and decline. Despite widespread changes to physiological mechanisms supporting perception and cognition, older adults’ language abilities are frequently well preserved. At the same time, the neural systems engaged to achieve this high level of success change, and individual differences in neural organization appear to differentiate between more and less successful performers. This chapter reviews anatomical and cognitive changes that occur in aging and popular frameworks for age-related changes in brain function, followed by an examination of how these principles play out in the context of language comprehension and production.

scholarly journals Twitter Sentiment Analysis towards COVID-19 Vaccines in the Philippines Using Naïve Bayes

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 204
Author(s):  
Charlyn Villavicencio ◽  
Julio Jerison Macrohon ◽  
X. Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
Data Science ◽  
Naive Bayes ◽  
The Philippines ◽  
Naïve Bayes ◽  
Social Networking Site ◽  
Bayes Model ◽  
The Government ◽  
Processing Techniques

A year into the COVID-19 pandemic and one of the longest recorded lockdowns in the world, the Philippines received its first delivery of COVID-19 vaccines on 1 March 2021 through WHO’s COVAX initiative. A month into inoculation of all frontline health professionals and other priority groups, the authors of this study gathered data on the sentiment of Filipinos regarding the Philippine government’s efforts using the social networking site Twitter. Natural language processing techniques were applied to understand the general sentiment, which can help the government in analyzing their response. The sentiments were annotated and trained using the Naïve Bayes model to classify English and Filipino language tweets into positive, neutral, and negative polarities through the RapidMiner data science software. The results yielded an 81.77% accuracy, which outweighs the accuracy of recent sentiment analysis studies using Twitter data from the Philippines.

Machine learning for rediscovering revolutionary ideas of the past

2021 ◽  
pp. 105971232098304
Author(s):  
R Alexander Bentley ◽  
Joshua Borycz ◽  
Simon Carrignon ◽  
Damian J Ruck ◽  
Michael J O’Brien
Keyword(s):  
Language Processing ◽  
Scientific Progress ◽  
Phylogenetic Inference ◽  
The Past ◽  
New Methods ◽  
Inherent Nature ◽  
Social Learning Processes ◽  
High Level ◽  
Scientific Ideas ◽  
Revolutionary Science

The explosion of online knowledge has made knowledge, paradoxically, difficult to find. A web or journal search might retrieve thousands of articles, ranked in a manner that is biased by, for example, popularity or eigenvalue centrality rather than by informed relevance to the complex query. With hundreds of thousands of articles published each year, the dense, tangled thicket of knowledge grows even more entwined. Although natural language processing and new methods of generating knowledge graphs can extract increasingly high-level interpretations from research articles, the results are inevitably biased toward recent, popular, and/or prestigious sources. This is a result of the inherent nature of human social-learning processes. To preserve and even rediscover lost scientific ideas, we employ the theory that scientific progress is punctuated by means of inspired, revolutionary ideas at the origin of new paradigms. Using a brief case example, we suggest how phylogenetic inference might be used to rediscover potentially useful lost discoveries, as a way in which machines could help drive revolutionary science.

scholarly journals Shall I Work with Them? A Knowledge Graph-Based Approach for Predicting Future Research Collaborations

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 664
Author(s):  
Nikos Kanakaris ◽  
Nikolaos Giarelis ◽  
Ilias Siachos ◽  
Nikos Karacapilidis
Keyword(s):  
Language Processing ◽  
Scientific Knowledge ◽  
Link Prediction ◽  
Performance Metrics ◽  
Future Research ◽  
Knowledge Graph ◽  
Prediction Problem ◽  
Textual Information ◽  
Research Collaborations ◽  
Processing Techniques

We consider the prediction of future research collaborations as a link prediction problem applied on a scientific knowledge graph. To the best of our knowledge, this is the first work on the prediction of future research collaborations that combines structural and textual information of a scientific knowledge graph through a purposeful integration of graph algorithms and natural language processing techniques. Our work: (i) investigates whether the integration of unstructured textual data into a single knowledge graph affects the performance of a link prediction model, (ii) studies the effect of previously proposed graph kernels based approaches on the performance of an ML model, as far as the link prediction problem is concerned, and (iii) proposes a three-phase pipeline that enables the exploitation of structural and textual information, as well as of pre-trained word embeddings. We benchmark the proposed approach against classical link prediction algorithms using accuracy, recall, and precision as our performance metrics. Finally, we empirically test our approach through various feature combinations with respect to the link prediction problem. Our experimentations with the new COVID-19 Open Research Dataset demonstrate a significant improvement of the abovementioned performance metrics in the prediction of future research collaborations.

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