Abstract: Text classification or categorization is the process of automatically tagging a textual document with most relevant labels or categories. When the number of labels is restricted to one, the task becomes single-label text categorization. However, the multi-label version is challenging. For Arabic language, both tasks (especially the latter one) become more challenging in the absence of large and free Arabic rich and rational datasets. We used sub data from a large Single-labeled Arabic News Articles Dataset (SANAD) of textual data collected from three news portals. The dataset is a large one consisting of almost 200k articles distributed into seven categories that we offer to the research community on Arabic computational linguistics.
Code and data in this link:
https://github.com/hezam2022/text-categorization
NLP Techniques for Text Classification.docxKevinSims18
Natural Language Processing (NLP) is an area of computer science and artificial intelligence that aims to enable machines to understand and interpret human language. Text classification is one of the most common tasks in NLP, and it involves categorizing text into predefined categories or classes. In this blog post, we will explore some of the most effective NLP techniques for text classification.
Survey of Machine Learning Techniques in Textual Document ClassificationIOSR Journals
Classification of Text Document points towards associating one or more predefined categories based
on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays
an important role in training the system with predefined categories. The importance of Machine learning
approach has felt because of which the study has been taken up for text document classification based on the
statistical event models available. The aim of this paper is to present the important techniques and
methodologies that are employed for text documents classification, at the same time making awareness of some
of the interesting challenges that remain to be solved, focused mainly on text representation and machine
learning techniques.
TEXT MINING-TAPPING HIDDEN KERNELS OF WISDOMITC Infotech
This paper discusses how automatic document classification, information retrieval, word frequency calculation, sentiment analysis, topic modelling and trend analysis can be utilized for root cause analysis, devising competitive strategies, enhancing customer experience and so on.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
NLP Techniques for Text Classification.docxKevinSims18
Natural Language Processing (NLP) is an area of computer science and artificial intelligence that aims to enable machines to understand and interpret human language. Text classification is one of the most common tasks in NLP, and it involves categorizing text into predefined categories or classes. In this blog post, we will explore some of the most effective NLP techniques for text classification.
Survey of Machine Learning Techniques in Textual Document ClassificationIOSR Journals
Classification of Text Document points towards associating one or more predefined categories based
on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays
an important role in training the system with predefined categories. The importance of Machine learning
approach has felt because of which the study has been taken up for text document classification based on the
statistical event models available. The aim of this paper is to present the important techniques and
methodologies that are employed for text documents classification, at the same time making awareness of some
of the interesting challenges that remain to be solved, focused mainly on text representation and machine
learning techniques.
TEXT MINING-TAPPING HIDDEN KERNELS OF WISDOMITC Infotech
This paper discusses how automatic document classification, information retrieval, word frequency calculation, sentiment analysis, topic modelling and trend analysis can be utilized for root cause analysis, devising competitive strategies, enhancing customer experience and so on.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEkevig
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It encompasses a range of techniques and technologies that enable machines to understand, interpret, and generate human language in a way that is meaningful and useful.
https://hiretopwriters.com/
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented "An Overview of Taxonomies and AI" on January 30th, 2024, in the inaugural webinar of the Artificial Intelligence webinar series: The promise and the perils,” hosted by the Knowledge & Information Management Group of CILIP, the library and information association of the UK. In her presentation, Heather explained, with examples, how both generative AI and other AI technologies support taxonomy development and use and how taxonomies can support AI applications.
Explore the presentation to learn:
Why both top-down and bottom-up methods are needed in taxonomy creation
What AI methods are used for auto-tagging and auto-classification with taxonomies
How AI methods can extract candidate terms for taxonomy creation
How generative AI can be used for certain bottom-up taxonomy development tasks
How AI can be used to analyze a taxonomy against a corpus of documents
How generative AI can be used in queries to analyze a taxonomy
What AI applications taxonomies can support
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
Post 1What is text analytics How does it differ from text mini.docxstilliegeorgiana
Post 1:
What is text analytics? How does it differ from text mining?
Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
Differences between Text Mining and Text Analytics:
• Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text.
• Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations.
• Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human.
• Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
• Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone.
2. What technologies were used in building Watson (both hardware and software)?
Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.
Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...
Post 1What is text analytics How does it differ from text minianhcrowley
Post 1:
What is text analytics? How does it differ from text mining?
Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
Differences between Text Mining and Text Analytics:
• Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text.
• Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations.
• Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human.
• Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
• Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone.
2. What technologies were used in building Watson (both hardware and software)?
Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.
Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
A Hybrid Approach for Supervised Twitter Sentiment Classification ....................................................1
K. Revathy and Dr. B. Sathiyabhama
A Survey of Dynamic Duty Cycle Scheduling Scheme at Media Access Control Layer for Energy
Conservation .....................................................................................................................................1
Prof. M. V. Nimbalkar and Sampada Khandare
A Survey on Privacy Preserving Data Mining Techniques ....................................................................1
A. K. Ilavarasi, B. Sathiyabhama and S. Poorani
An Ontology Based System for Predicting Disease using SWRL Rules ...................................................1
Mythili Thirugnanam, Tamizharasi Thirugnanam and R. Mangayarkarasi
Performance Evaluation of Web Services in C#, JAVA, and PHP ..........................................................1
Dr. S. Sagayaraj and M. Santhosh Kumar
Semi-Automated Polyhouse Cultivation Using LabVIEW......................................................................1
Prathiba Jonnala and Sivaji Satrasupalli
Performance of Biometric Palm Print Personal Identification Security System Using Ordinal Measures 1
V. K. Narendira Kumar and Dr. B. Srinivasan
MIMO System for Next Generation Wireless Communication..............................................................1
Sharif, Mohammad Emdadul Haq and Md. Arif Rana
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
A Survey on Sentiment Categorization of Movie ReviewsEditor IJMTER
Sentiment categorization is a process of mining user generated text content and determine
the sentiment of the users towards that particular thing. It is the approach of detecting the sentiment of
the author in regard to some topics. It also known as sentiment detection, sentiment analysis and opinion
mining. It is very useful for movie production companies that interested in knowing how users feel
about their movies. For example word “excellent” indicates that the review gives positive emotion about
particular movie. The same applies to movies, songs, cars, holiday destinations, Political parties, social
network sites, web blogs, discussion forum and so on. Sentiment categorization can be carried out by
using three approaches. First, Supervised machine learning based text classifier on Naïve Bayes,
Maximum Entropy, SVM, kNN classifier, hidden marcov model. Second, Unsupervised Semantic
Orientation scheme of extracting relevant N-grams of the text and then labelling. Third, SentiWordNet
based publicly available library.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEkevig
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
Neural Network Based Context Sensitive Sentiment AnalysisEditor IJCATR
Social media communication is evolving more in these days. Social networking site is being rapidly increased in recent years, which provides platform to connect people all over the world and share their interests. The conversation and the posts available in social media are unstructured in nature. So sentiment analysis will be a challenging work in this platform. These analyses are mostly performed in machine learning techniques which are less accurate than neural network methodologies. This paper is based on sentiment classification using Competitive layer neural networks and classifies the polarity of a given text whether the expressed opinion in the text is positive or negative or neutral. It determines the overall topic of the given text. Context independent sentences and implicit meaning in the text are also considered in polarity classification.
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It encompasses a range of techniques and technologies that enable machines to understand, interpret, and generate human language in a way that is meaningful and useful.
https://hiretopwriters.com/
Heather Hedden, Senior Consultant at Enterprise Knowledge, presented "An Overview of Taxonomies and AI" on January 30th, 2024, in the inaugural webinar of the Artificial Intelligence webinar series: The promise and the perils,” hosted by the Knowledge & Information Management Group of CILIP, the library and information association of the UK. In her presentation, Heather explained, with examples, how both generative AI and other AI technologies support taxonomy development and use and how taxonomies can support AI applications.
Explore the presentation to learn:
Why both top-down and bottom-up methods are needed in taxonomy creation
What AI methods are used for auto-tagging and auto-classification with taxonomies
How AI methods can extract candidate terms for taxonomy creation
How generative AI can be used for certain bottom-up taxonomy development tasks
How AI can be used to analyze a taxonomy against a corpus of documents
How generative AI can be used in queries to analyze a taxonomy
What AI applications taxonomies can support
UNIT V TEXT AND OPINION MINING
Text Mining in Social Networks -Opinion extraction – Sentiment classification and clustering -
Temporal sentiment analysis - Irony detection in opinion mining - Wish analysis – Product review mining – Review Classification – Tracking sentiments towards topics over time
Post 1What is text analytics How does it differ from text mini.docxstilliegeorgiana
Post 1:
What is text analytics? How does it differ from text mining?
Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
Differences between Text Mining and Text Analytics:
• Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text.
• Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations.
• Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human.
• Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
• Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone.
2. What technologies were used in building Watson (both hardware and software)?
Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.
Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...
Post 1What is text analytics How does it differ from text minianhcrowley
Post 1:
What is text analytics? How does it differ from text mining?
Text Analytics is applying of statistical and machine learning techniques to be able to predict /prescribe or infer any information from the text-mined data. Text mining is a tool that helps in getting the data cleaned up.Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
Differences between Text Mining and Text Analytics:
• Text Mining and Text Analytics solve the same problems, but use different techniques and are complementary ways to automatically extract meaning from text.
• Text Analytics is developed within the field of computational linguistics. It has the ability to encode human understanding into a series of linguistic rules which are generated by humans are high in precision, but they do not automatically adapt and are usually fragile when tried in new situations.
• Text mining is a newer discipline arising out of the fields of statistics, data mining, and machine learning. Its strength is the ability to inductively create models from collections of historical data. Because statistical models are learned from training data they are adaptive and can identify “unknown unknowns”, leading to the better recall. Still, they can be prone to missing something that would seem obvious to a human.
• Text analytics and text mining approaches have essentially equivalent performance. Text analytics requires an expert linguist to produce complex rule sets, whereas text mining requires the analyst to hand-label cases with outcomes or classes to create training data.
• Due to their different perspectives and strengths, combining text analytics with text mining often leads to better performance than either approach alone.
2. What technologies were used in building Watson (both hardware and software)?
Watson is an extraordinary computer system (a novel combination of advanced hardware an software) designed at answering questions posed in natural human language.Watson is an artificially intelligent computer system capable of answering questions posed in natural language, developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.
Watson received the first prize of $1 million.The goal was to advance computer science by exploring new ways for computer technology to affect science, business, and society.IBM undertook a challenge to build a computer system that could compete at the human champion level in real time on the American TV quiz show Jeopardy!The extent of the challenge in ...
professional fuzzy type-ahead rummage around in xml type-ahead search techni...Kumar Goud
Abstract – It is a research venture on the new information-access standard called type-ahead search, in which systems discover responds to a keyword query on-the-fly as users type in the uncertainty. In this paper we learn how to support fuzzy type-ahead search in XML. Underneath fuzzy search is important when users have limited knowledge about the exact representation of the entities they are looking for, such as people records in an online directory. We have developed and deployed several such systems, some of which have been used by many people on a daily basis. The systems received overwhelmingly positive feedbacks from users due to their friendly interfaces with the fuzzy-search feature. We describe the design and implementation of the systems, and demonstrate several such systems. We show that our efficient techniques can indeed allow this search paradigm to scale on large amounts of data.
Index Terms - type-ahead, large data set, server side, online directory, search technique.
Methods for Sentiment Analysis: A Literature Studyvivatechijri
Sentiment analysis is a trending topic, as everyone has an opinion on everything. The systematic
study of these opinions can lead to information which can prove to be valuable for many companies and
industries in future. A huge number of users are online, and they share their opinions and comments regularly,
this information can be mined and used efficiently. Various companies can review their own product using
sentiment analysis and make the necessary changes in future. The data is huge and thus it requires efficient
processing to collect this data and analyze it to produce required result.
In this paper, we will discuss the various methods used for sentiment analysis. It also covers various techniques
used for sentiment analysis such as lexicon based approach, SVM [10], Convolution neural network,
morphological sentence pattern model [1] and IML algorithm. This paper shows studies on various data sets
such as Twitter API, Weibo, movie review, IMDb, Chinese micro-blog database [9] and more. The paper shows
various accuracy results obtained by all the systems.
Develop a robust and effective book recommendation system that provides personalized suggestions to users, enhancing their reading experience and promoting diverse literary exploration.
A Hybrid Approach for Supervised Twitter Sentiment Classification ....................................................1
K. Revathy and Dr. B. Sathiyabhama
A Survey of Dynamic Duty Cycle Scheduling Scheme at Media Access Control Layer for Energy
Conservation .....................................................................................................................................1
Prof. M. V. Nimbalkar and Sampada Khandare
A Survey on Privacy Preserving Data Mining Techniques ....................................................................1
A. K. Ilavarasi, B. Sathiyabhama and S. Poorani
An Ontology Based System for Predicting Disease using SWRL Rules ...................................................1
Mythili Thirugnanam, Tamizharasi Thirugnanam and R. Mangayarkarasi
Performance Evaluation of Web Services in C#, JAVA, and PHP ..........................................................1
Dr. S. Sagayaraj and M. Santhosh Kumar
Semi-Automated Polyhouse Cultivation Using LabVIEW......................................................................1
Prathiba Jonnala and Sivaji Satrasupalli
Performance of Biometric Palm Print Personal Identification Security System Using Ordinal Measures 1
V. K. Narendira Kumar and Dr. B. Srinivasan
MIMO System for Next Generation Wireless Communication..............................................................1
Sharif, Mohammad Emdadul Haq and Md. Arif Rana
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
2. Outline
Introduction
What is Text Classification?
Why is Text Classification Important?
How Does Text Classification Work?
Arabic Language Characteristics
Classification General Phases
Datasets
Experiments dataset
The first experiment: Machine Learning
The second experiment: Machine Learning and Deep Learning
Conclusion
3. Introduction
Finding useful knowledge on a given subject in a vast volume of
online textual data that is rapidly growing is a difficult challenge.
To solve this issue, organize data into predetermined categories
could help.
Algorithms of text classification are the basis of many applications for
natural language processing, such as text description, query
response, detection of spam, and visualization of text.
While Arabic language on the internet is rising increasingly, its
content is still as poor as 3 percent.
For researchers and developers, the recent rapid growth is a
convincing incentive to develop successful frameworks and tools to
advance study in Arabic NLP. The automated mapping of texts to
predefine marks or classes is text categorization.
4. Introduction
Deep Learning (DL) and Machine Learning (ML) models were used
to enhance text classification for Arabic language.
Fig. 1. ML vs DL.
5. What is Text Classification?
Text classification is a machine learning technique that assigns a set
of predefined categories to open-ended text.
Text classifiers can be used to organize, structure, and categorize
pretty much any kind of text – from documents, medical studies and
files, and all over the web.
For example, new articles can be organized by topics; support
tickets can be organized by urgency; chat conversations can be
organized by language; brand mentions can be organized by
sentiment; and so on.
Text classification is one of the fundamental tasks in NLP with broad
applications such as sentiment analysis, topic labeling, spam
detection, and intent detection.
6. What is Text Classification?
Here’s an example of how it works:
“The user interface is quite straightforward and easy to use.”
A text classifier can take this phrase as an input, analyze its content, and
then automatically assign relevant tags, such as UI and Easy To Use.
7. Why is Text Classification Important?
It’s estimated that around 80% of all information is unstructured, with
text being one of the most common types of unstructured data.
Because of the messy nature of text, analyzing, understanding,
organizing, and sorting through text data is hard and time-
consuming, so most companies fail to use it to its full potential.
This is where text classification with machine learning comes in.
Using text classifiers, companies can automatically structure all
manner of relevant text, from emails, legal documents, social media,
chatbots, surveys, and more in a fast and cost-effective way.
This allows companies to save time analyzing text data, automate
business processes, and make data-driven business decisions.
8. Why is Text Classification Important?
Why use machine learning text classification? Some of the top
reasons:
Scalability
Manually analyzing and organizing is slow and much less accurate..
Machine learning can automatically analyze millions of surveys,
comments, emails, etc., at a fraction of the cost, often in just a few
minutes.
Text classification tools are scalable to any business needs, large or
small.
9. Why is Text Classification Important?
Real-time analysis
There are critical situations that companies need to identify as soon as
possible and take immediate action (e.g., PR crises on social media).
Machine learning text classification can follow your brand mentions
constantly and in real time, so you'll identify critical information and be
able to take action right away.
10. Why is Text Classification Important?
Consistent criteria
Human annotators make mistakes when classifying text data due to
distractions, fatigue, and boredom, and human subjectivity creates
inconsistent criteria.
Machine learning, on the other hand, applies the same lens and criteria
to all data and results. Once a text classification model is properly
trained it performs with unsurpassed accuracy.
11. How Does Text Classification Work?
You can perform text classification in two ways: manual or automatic.
Manual text classification involves a human annotator, who interprets
the content of text and categorizes it accordingly. This method can
deliver good results but it’s time-consuming and expensive.
Automatic text classification applies machine learning, natural
language processing (NLP), and other AI-guided techniques to
automatically classify text in a faster, more cost-effective, and more
accurate manner.
12. How Does Text Classification Work?
In this guide, we’re going to focus on automatic text classification.
There are many approaches to automatic text classification, but they
all fall under three types of systems:
Rule-based systems
Machine learning-based systems
Hybrid systems
13. Rule-based systems
Rule-based approaches classify text into organized groups by using
a set of handcrafted linguistic rules. These rules instruct the system
to use semantically relevant elements of a text to identify relevant
categories based on its content. Each rule consists of an antecedent
or pattern and a predicted category.
Say that you want to classify news articles into two
groups: Sports and Politics. First, you’ll need to define two lists of
words that characterize each group (e.g., words related to sports
such as football, basketball, LeBron James, etc., and words related
to politics, such as Donald Trump, Hillary Clinton, Putin, etc.).
14. Rule-based systems
Next, when you want to classify a new incoming text, you’ll need to
count the number of sport-related words that appear in the text and
do the same for politics-related words. If the number of sports-related
word appearances is greater than the politics-related word count,
then the text is classified as Sports and vice versa.
Rule-based systems are human comprehensible and can be
improved over time. But this approach has some disadvantages. For
starters, these systems require deep knowledge of the domain. They
are also time-consuming, since generating rules for a complex
system can be quite challenging and usually requires a lot of analysis
and testing. Rule-based systems are also difficult to maintain and
don’t scale well given that adding new rules can affect the results of
the pre-existing rules.
15. Machine learning based systems
Instead of relying on manually crafted rules, machine learning text
classification learns to make classifications based on past
observations. By using pre-labeled examples as training data,
machine learning algorithms can learn the different associations
between pieces of text, and that a particular output (i.e., tags) is
expected for a particular input (i.e., text). A “tag” is the pre-
determined classification or category that any given text could fall
into.
Then, the machine learning algorithm is fed with training data that
consists of pairs of feature sets (vectors for each text example) and
tags (e.g. sports, politics) to produce a classification model.
Once it’s trained with enough training samples, the machine learning
model can begin to make accurate predictions.
17. Text Classification Algorithms
Machine Learning: Some of the most popular text classification
algorithms include the Naive Bayes family of algorithms, support
vector machines (SVM), and deep learning.
Naive Bayes
The Naive Bayes family of statistical algorithms are some of the
most used algorithms in text classification and text analysis,
overall.
One of the members of that family is Multinomial Naive Bayes
(MNB) with a huge advantage, that you can get really good results
even when your dataset isn’t very large (~ a couple of thousand
tagged samples) and computational resources are scarce.
https://monkeylearn.com/text-classification/
18. Text Classification Algorithms
Support Vector Machines
Support Vector Machines (SVM) is another powerful text classification
machine learning algorithm, becauseike Naive Bayes, SVM doesn’t
need much training data to start providing accurate results. SVM does,
however, require more computational resources than Naive Bayes, but
the results are even faster and more accurate.
In short, SVM draws a line or “hyperplane” that divides a space into two
subspaces. One subspace contains vectors (tags) that belong to a
group, and another subspace contains vectors that do not belong to that
group.
19. Text Classification Algorithms
Support Vector Machines
Support Vector Machines (SVM) is another powerful text classification
machine learning algorithm, becauseike Naive Bayes, SVM doesn’t
need much training data to start providing accurate results. SVM does,
however, require more computational resources than Naive Bayes, but
the results are even faster and more accurate.
In short, SVM draws a line or “hyperplane” that divides a space into two
subspaces. One subspace contains vectors (tags) that belong to a
group, and another subspace contains vectors that do not belong to that
group.
20. Text Classification Algorithms
Deep Learning : Deep learning is a set of algorithms and techniques
inspired by how the human brain works, called neural networks.
Deep learning architectures offer huge benefits for text classification
because they perform at super high accuracy with lower-level
engineering and computation.
The two main deep learning architectures for text classification
are Convolutional Neural Networks (CNN) and Recurrent Neural
Networks (RNN).
Deep learning algorithms do require much more training data than
traditional machine learning algorithms (at least millions of tagged
examples).
21. Hybrid Systems
Hybrid systems combine a machine learning-trained base classifier
with a rule-based system, used to further improve the results.
These hybrid systems can be easily fine-tuned by adding specific
rules for those conflicting tags that haven’t been correctly modeled by
the base classifier.
22. Arabic Language Characteristics
Arabic language is the fifth most commonly spoken language in the world
and the fifth most frequently used on the internet. More than 422 million
speak Arabic language (by more than 6.0% of the global population.
Letters of Arabic language consist of 28 letters plus hamza.
Arabic language letters are written from right to left.
One of the main characteristics of Arabic language is that its letters has
different forms and shapes depending on the position of the letter. one of
the excellent merits of Arabic language is that majority of Arabic word has
a root. Representing words with its root helps in reducing the number of
words.
There are several forms of grammatical, variations of synonyms word, and
numerous meanings of word in the Arabic language, which differ based on
factors such as order of the word.
25. Datasets
SANAD Dataset is a large collection of Arabic news articles that can be
used in different Arabic NLP tasks such as Text Classification and Word
Embedding. The articles were collected using Python scripts written
specifically for three popular news websites: AlKhaleej, AlArabiya and
Akhbarona.
All datasets have seven categories [Culture, Finance, Medical, Politics,
Religion, Sports and Tech], except AlArabiya which doesn’t have
[Religion]. SANAD contains a total number of 190k+ articles.
SANAD has a total number of 194,797 articles categorized and formatted
as shown in Fig.2
In general, SANAD adopted the annotation of each article as appeared in
its news portal source. Only one collection of articles is manually re-
labeled to enrich the ‘politics’ category in AlArabiya dataset.
27. Experiments dataset
A subset Khaleej (45500 articles in 7 categories) of SANAD. Labels
are categorized in: Culture, Finance, Medical,
Politics,Religion,Sports,Tech.
Fig. 6. A subset Khaleej.
28. The first experiment: Machine Learning
Arabic Text Preprocessing
Fig. 7. Extracting data from the folders and files.
29. The first experiment: Machine Learning
Arabic Text Preprocessing
Fig. 8. Read Dataset.
30. The first experiment: Machine Learning
Arabic Text Preprocessing
Fig. 9. Exploratory Data Analysis.
31. The first experiment: Machine Learning
Arabic Text Preprocessing
Fig. 10. Remove stopwords.
32. The first experiment: Machine Learning
Arabic Text Preprocessing
Fig. 11. Normalization.
33. The first experiment: Machine Learning
Arabic Text Preprocessing
Fig. 12. Abstract article (Remove Punctuations and Noise Removal (Remove extra whitespace, Remove numbers)).
40. The second experiment: Machine Learning and
Deep Learning
Applying ML and DL
Table. 2. Reslut2.
Model Accuracy Our Accuracy
Naive Bayes 83.00% 90.50%
RNN 85.00% 43.30%
LSTM 91.00% 95.30%
GRU 90.00% 95.40%
BERT 90.00% -
DistilBERT 89.37% -
RoBERTa 78.70% -
41. The second experiment: Machine Learning and
Deep Learning
Applying ML and DL
Fig. 18. Reslut2.
42. Conclusion
Algorithms of text classification are the basis of many applications for natural language
processing, such as text description, query response, detection of spam, and
visualization of text. Arabic language on the internet is rising increasingly, but its
content is still as poor as 3 percent. Few studies have been done to categorize and
classify the Arabic language.
In the first experiment, the highest result they achieved in the Logistic Regression
algorithm was 93.73%accuracy, and the highest result they achieved in the Logistic
Regression algorithm was 90% accuracy.
In the second experiment, the highest result they achieved in the Logistic LSTM was
91.00%accuracy, and the highest result they achieved in the GRU algorithm was
95.40%accuracy
Editor's Notes
إن العثور على معرفة مفيدة حول موضوع معين في حجم كبير من البيانات النصية عبر الإنترنت التي تنمو بسرعة يمثل تحديًا صعبًا.
لحل هذه المشكلة، قد يساعد تنظيم البيانات في فئات محددة مسبقًا.
تعتبر خوارزميات تصنيف النص أساس العديد من تطبيقات معالجة اللغة الطبيعية، مثل وصف النص، والاستجابة للاستعلام، والكشف عن البريد العشوائي، وتصور النص.
وفي حين أن اللغة العربية على الإنترنت آخذة في الارتفاع بشكل متزايد، إلا أن محتواها لا يزال ضعيفًا بنسبة 3 بالمائة.
بالنسبة للباحثين والمطورين، يعد النمو السريع الأخير حافزًا مقنعًا لتطوير أطر وأدوات ناجحة لتعزيز الدراسة في البرمجة اللغوية العصبية العربية. التعيين الآلي للنصوص لتحديد العلامات أو الفئات مسبقًا هو تصنيف النص.
تصنيف النص هو أسلوب للتعلم الآلي تعين مجموعة من الفئات المحددة مسبقًا لنص مفتوح.ذ
يمكن استخدام مصنفات النصوص لتنظيم وهيكلة وتصنيف أي نوع من النصوص تقريبًا - من المستندات والدراسات الطبية والملفات، وفي جميع أنحاء الويب.
على سبيل المثال، يمكن تنظيم المقالات الجديدة حسب المواضيع؛ يمكن تنظيم تذاكر الدعم حسب الضرورة؛ يمكن تنظيم محادثات الدردشة حسب اللغة؛ يمكن تنظيم إشارات العلامة التجارية حسب المشاعر؛ وما إلى ذلك وهلم جرا.
يعد تصنيف النص إحدى المهام الأساسية في معالجة اللغة الطبيعية من خلال تطبيقات واسعة مثل تحليل المشاعر، وتصنيف المواضيع، واكتشاف الرسائل غير المرغوب فيها، واكتشاف النوايا.
فيما يلي مثال لكيفية عمله:
"واجهة المستخدم واضحة تمامًا وسهلة الاستخدام."
يمكن لمصنف النص أن يأخذ هذه العبارة كمدخل، ويحلل محتواها، ثم يعين العلامات ذات الصلة تلقائيًا، مثل واجهة المستخدم وسهلة الاستخدام.
تشير التقديرات إلى أن حوالي 80% من جميع المعلومات غير منظمة، حيث يعد النص أحد أكثر أنواع البيانات غير المنظمة شيوعًا. نظرًا للطبيعة الفوضوية للنص، فإن تحليل البيانات النصية وفهمها وتنظيمها وفرزها يعد أمرًا صعبًا ويستغرق وقتًا طويلاً، لذلك تفشل معظم الشركات في استخدامه إلى أقصى إمكاناته.
هذا هو المكان الذي يأتي فيه تصنيف النص باستخدام التعلم الآلي. باستخدام مصنفات النصوص، يمكن للشركات هيكلة جميع أنواع النصوص ذات الصلة تلقائيًا، من رسائل البريد الإلكتروني والمستندات القانونية ووسائل التواصل الاجتماعي وروبوتات الدردشة والاستطلاعات والمزيد بطريقة سريعة وفعالة من حيث التكلفة.
يتيح ذلك للشركات توفير الوقت في تحليل البيانات النصية، وأتمتة العمليات التجارية، واتخاذ قرارات تجارية تعتمد على البيانات.
لماذا نستخدم تصنيف نص التعلم الآلي؟ بعض أهم الأسباب:
قابلية التوسع
يعد التحليل والتنظيم يدويًا بطيئًا وأقل دقة بكثير. ويمكن للتعلم الآلي أن يحلل تلقائيًا ملايين الاستطلاعات والتعليقات ورسائل البريد الإلكتروني وما إلى ذلك، مقابل جزء صغير من التكلفة، وغالبًا ما يكون ذلك في بضع دقائق فقط. أدوات تصنيف النص قابلة للتطوير لتناسب أي احتياجات عمل، كبيرة كانت أم صغيرة.
التحليل في الوقت الحقيقي
هناك مواقف حرجة تحتاج الشركات إلى تحديدها في أقرب وقت ممكن واتخاذ إجراءات فورية (على سبيل المثال، أزمات العلاقات العامة على وسائل التواصل الاجتماعي). يمكن أن يتبع تصنيف نص التعلم الآلي الإشارات إلى علامتك التجارية باستمرار وفي الوقت الفعلي، لذلك ستحدد المعلومات المهمة وتكون قادرًا على اتخاذ الإجراء على الفور.
معايير متسقة
يرتكب المدونون البشريون أخطاء عند تصنيف البيانات النصية بسبب التشتيت والتعب والملل، وتخلق الذاتية البشرية معايير غير متسقة. ومن ناحية أخرى، يطبق التعلم الآلي نفس العدسة والمعايير على جميع البيانات والنتائج. بمجرد تدريب نموذج تصنيف النص بشكل صحيح، فإنه يعمل بدقة غير مسبوقة.
معايير متسقة
يرتكب المدونون البشريون أخطاء عند تصنيف البيانات النصية بسبب التشتيت والتعب والملل، وتخلق الذاتية البشرية معايير غير متسقة.
من ناحية أخرى، يطبق التعلم الآلي نفس العدسة والمعايير على جميع البيانات والنتائج. بمجرد تدريب نموذج تصنيف النص بشكل صحيح، فإنه يعمل بدقة غير مسبوقة.
يمكنك إجراء تصنيف النص بطريقتين: يدوي أو تلقائي.
يتضمن التصنيف اليدوي للنص مُعلقًا بشريًا، والذي يفسر محتوى النص ويصنفه وفقًا لذلك. يمكن أن تحقق هذه الطريقة نتائج جيدة ولكنها تستغرق وقتًا طويلاً ومكلفة.
يطبق التصنيف التلقائي للنص التعلم الآلي ومعالجة اللغة الطبيعية (NLP) وغيرها من التقنيات الموجهة بالذكاء الاصطناعي لتصنيف النص تلقائيًا بطريقة أسرع وأكثر فعالية من حيث التكلفة وأكثر دقة.
في هذا الدليل، سنركز على التصنيف التلقائي للنص.
هناك العديد من الأساليب لتصنيف النص التلقائي، ولكنها تندرج جميعها تحت ثلاثة أنواع من الأنظمة:
الأنظمة المبنية على القواعد
الأنظمة القائمة على التعلم الآلي
الأنظمة الهجينة
تقوم الأساليب المبنية على القواعد بتصنيف النص إلى مجموعات منظمة باستخدام مجموعة من القواعد اللغوية المصنوعة يدويًا. توجه هذه القواعد النظام لاستخدام العناصر ذات الصلة لغويًا في النص لتحديد الفئات ذات الصلة بناءً على محتواه. تتكون كل قاعدة من سابقة أو نمط وفئة متوقعة.
لنفترض أنك تريد تصنيف المقالات الإخبارية إلى مجموعتين: الرياضة والسياسة . أولاً، ستحتاج إلى تحديد قائمتين من الكلمات التي تميز كل مجموعة (على سبيل المثال، الكلمات المتعلقة بالرياضة مثل كرة القدم وكرة السلة وليبرون جيمس وما إلى ذلك، والكلمات المرتبطة بالسياسة ، مثل دونالد ترامب وهيلاري كلينتون وبوتين ، إلخ.).
بعد ذلك، عندما تريد تصنيف نص وارد جديد، ستحتاج إلى حساب عدد الكلمات المتعلقة بالرياضة التي تظهر في النص والقيام بنفس الشيء بالنسبة للكلمات المتعلقة بالسياسة. إذا كان عدد ظهور الكلمات المتعلقة بالرياضة أكبر من عدد الكلمات المتعلقة بالسياسة، فسيتم تصنيف النص على أنه رياضة والعكس صحيح.
أنظمة القائمة على القواعد قابلة للفهم البشري ويمكن تحسينها بمرور الوقت. لكن هذا النهج له بعض العيوب. بالنسبة للمبتدئين، تتطلب هذه الأنظمة معرفة عميقة بالمجال. كما أنها تستغرق وقتًا طويلاً، نظرًا لأن إنشاء قواعد لنظام معقد يمكن أن يكون أمرًا صعبًا للغاية وعادةً ما يتطلب الكثير من التحليل والاختبار. من الصعب أيضًا الحفاظ على الأنظمة القائمة على القواعد ولا يمكن توسيع نطاقها بشكل جيد نظرًا لأن إضافة قواعد جديدة يمكن أن يؤثر على نتائج القواعد الموجودة مسبقًا.
بدلاً من الاعتماد على القواعد المعدة يدويًا، يتعلم تصنيف النص بالتعلم الآلي كيفية إجراء التصنيفات بناءً على الملاحظات السابقة. باستخدام الأمثلة المعنونة مسبقًا كبيانات تدريب، يمكن لخوارزميات التعلم الآلي أن تتعلم الارتباطات المختلفة بين أجزاء النص، وأن مخرجات معينة (على سبيل المثال، العلامات) متوقعة لمدخل معين (على سبيل المثال، النص). "العلامة" هي التصنيف أو الفئة المحددة مسبقًا والتي يمكن أن يندرج فيها أي نص معين.
بعد ذلك، يتم تغذية خوارزمية التعلم الآلي ببيانات التدريب التي تتكون من أزواج من مجموعات الميزات (ناقلات لكل مثال نصي ) والعلامات (مثل الرياضة والسياسة ) لإنتاج نموذج تصنيف.
بمجرد تدريبه بعينات تدريب كافية، يمكن لنموذج التعلم الآلي أن يبدأ في عمل تنبؤات دقيقة.
التعلم العميق عبارة عن مجموعة من الخوارزميات والتقنيات المستوحاة من كيفية عمل الدماغ البشري، تسمى الشبكات العصبية. تقدم بنيات التعلم العميق فوائد كبيرة لتصنيف النص لأنها تؤدي دقة عالية للغاية مع هندسة وحسابات منخفضة المستوى.
إن معماريتي التعلم العميق الرئيسيتين لتصنيف النص هما الشبكات العصبية التلافيفيةCNN الشبكات العصبية المتكررة RNN
تتطلب خوارزميات التعلم العميق بيانات تدريب أكثر بكثير من خوارزميات التعلم الآلي التقليدية (على الأقل ملايين الأمثلة ذات العلامات).
تجمع الأنظمة الهجينة بين مصنف أساسي تم تدريبه على التعلم الآلي ونظام قائم على القواعد، يُستخدم لتحسين النتائج بشكل أكبر.
يمكن ضبط هذه الأنظمة المختلطة بسهولة عن طريق إضافة قواعد محددة لتلك العلامات المتعارضة التي لم يتم تصميمها بشكل صحيح بواسطة المصنف الأساسي.
اللغة العربية هي اللغة الخامسة الأكثر استخدامًا في العالم والخامسة الأكثر استخدامًا على الإنترنت. يتحدث اللغة العربية أكثر من 422 مليون شخص (أي أكثر من 6.0% من سكان العالم).
حروف اللغة العربية تتكون من 28 حرف بالإضافة إلى الهمزة.
حروف اللغة العربية تكتب من اليمين إلى اليسار.
من الخصائص الرئيسية للغة العربية أن حروفها لها أشكال وأشكال مختلفة حسب موضع الحرف. من المزايا الممتازة للغة العربية أن غالبية الكلمات العربية لها جذر. تمثيل الكلمات بجذرها يساعد في تقليل عدد الكلمات.
هناك عدة أشكال نحوية، واختلافات في مرادفات الكلمات، ومعاني عديدة للكلمة في اللغة العربية، والتي تختلف بناءً على عوامل مثل ترتيب الكلمة.
مجموعة بيانات سند هي مجموعة كبيرة من المقالات الإخبارية العربية التي يمكن استخدامها في مهام البرمجة اللغوية العصبية العربية المختلفة مثل تصنيف النص وتضمين الكلمات. تم جمع المقالات باستخدام نصوص بايثون المكتوبة خصيصًا لثلاثة مواقع إخبارية شهيرة: الخليج والعربية وأخبارنا.
تحتوي جميع مجموعات البيانات على سبع فئات [الثقافة، والمالية، والطب، والسياسة، والدين، والرياضة، والتكنولوجيا]، باستثناء قناة العربية التي لا تحتوي على [الدين]. يحتوي سند على إجمالي عدد أكثر من 190 ألف مقالة.
يحتوي سند على إجمالي 194,797 مقالة مصنفة ومنسقة كما هو موضح في الشكل 1
وبشكل عام، اعتمدت سند الحاشية لكل مقال كما ورد في مصدر بوابتها الإخبارية. تمت إعادة تسمية مجموعة واحدة فقط من المقالات يدويًا لإثراء فئة "السياسة" في مجموعة بيانات العربية.
مجموعة فرعية من الخليج (45500 مقالة في 7 فئات) من سند. يتم تصنيف التسميات في: الثقافة، المالية، الطبية، السياسة، الدين، الرياضة، التكنولوجيا.
تعتبر خوارزميات تصنيف النص أساس العديد من تطبيقات معالجة اللغة الطبيعية، مثل وصف النص، والاستجابة للاستعلام، والكشف عن البريد العشوائي، وتصور النص. اللغة العربية على الإنترنت آخذة في الارتفاع بشكل متزايد، ولكن محتواها لا يزال ضعيفا بنسبة 3 في المائة. لم يتم إجراء سوى القليل من الدراسات لتصنيف اللغة العربية وتصنيفها.
في التجربة الأولى كانت أعلى نتيجة حققوها في خوارزمية الانحدار اللوجستي دقة 93.73%، وأعلى نتيجة حققوها في خوارزمية الانحدار اللوجستي كانت دقة 90%.
وفي التجربة الثانية، أعلى نتيجة حققوها في LSTM اللوجستية كانت دقة 91.00%، وأعلى نتيجة حققوها في خوارزمية GRU كانت دقة 95.40%.