Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
Process the sentiments of NLP with Naive Bayes Rule, Random Forest, Support Vector Machine, and much more.
Thanks, for your time, if you enjoyed this short slide there are tons of topics in advanced analytics, data science, and machine learning available in my medium repo. https://medium.com/@bobrupakroy
This Project Aimed at doing a comprehensive study of Different Machine Learning Approaches on Sentiment Analysis of Movie Reviews. Support Vector Machines were the one that Performed Most Accurately with Radial Basis Function. Lots of Other kernel functions and Kernel Parameters were tried to find the optimal one. We achieved accuracy up to 83%.
Supervised Learning Based Approach to Aspect Based Sentiment AnalysisTharindu Kumara
Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews) discussing a particular entity (e.g., a new model of a laptop). The systems attempt to
identify the main (e.g., the most frequently discussed) aspects (features) of the entity (e.g., battery, screen) and to estimate the average sentiment of the texts per aspect (e.g., how positive or negative the opinions are on average for each aspect).
This presentation educates you about Sentimental Analysis, What is sentiment analysis used for?, Challenges of sentiment analysis, How is sentiment analysis done? and Sentiment analysis algorithms.
For more topics stay tuned with Learnbay.
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
Searching for solutions,Uniformed search stategies,Informed search strategies,Local search algrithms and optimistic problems,Adversial Search,Search for games and Alpha-Beta pruning.
Supervised Learning Based Approach to Aspect Based Sentiment AnalysisTharindu Kumara
Aspect Based Sentiment Analysis (ABSA) systems receive as input a set of texts (e.g., product reviews) discussing a particular entity (e.g., a new model of a laptop). The systems attempt to
identify the main (e.g., the most frequently discussed) aspects (features) of the entity (e.g., battery, screen) and to estimate the average sentiment of the texts per aspect (e.g., how positive or negative the opinions are on average for each aspect).
This presentation educates you about Sentimental Analysis, What is sentiment analysis used for?, Challenges of sentiment analysis, How is sentiment analysis done? and Sentiment analysis algorithms.
For more topics stay tuned with Learnbay.
Review of Natural Language Processing tasks and examples of why it is so hard. Then he describes in detail text categorization and particularly sentiment analysis. A few common approaches for predicting sentiment are discussed, going even further, explaining statistical machine learning algorithms.
Sentiment analysis is essential operation to understand the polarity of particular text, blog etc. This presentation has introduction to SA and the approaches in which they can be designed.
Searching for solutions,Uniformed search stategies,Informed search strategies,Local search algrithms and optimistic problems,Adversial Search,Search for games and Alpha-Beta pruning.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
3. Searching for solutions.
Search Algorithms in Artificial Intelligence
Search algorithms are one of the most important areas of
Artificial Intelligence. This topic will explain all about the
search algorithms in AI.
Problem-solving agents:
In Artificial Intelligence, Search techniques are universal
problem-solving methods.
Rational agents or Problem-solving agents in AI mostly
used these search strategies or algorithms to solve a specific
problem and provide the best result.
Problem-solving agents are the goal-based agents and use
atomic representation. In this topic, we will learn various
problem-solving search algorithms.
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4. Search Algorithm Terminologies:
Search: Searching is a step by step procedure to solve a search-problem in
a given search space. A search problem can have three main factors:
Search Space: Search space represents a set of possible solutions, which a system may
have.
Start State: It is a state from where agent begins the search.
Goal test: It is a function which observe the current state and returns whether the goal
state is achieved or not.
Search tree: A tree representation of search problem is called Search tree.
The root of the search tree is the root node which is corresponding to the
initial state.
Actions: It gives the description of all the available actions to the agent.
Transition model: A description of what each action do, can be
represented as a transition model.
Path Cost: It is a function which assigns a numeric cost to each path.
Solution: It is an action sequence which leads from the start node to the
goal node.
Optimal Solution: If a solution has the lowest cost among all solutions.
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5. Properties of Search Algorithms:
Following are the four essential properties of search
algorithms to compare the efficiency of these algorithms:
Completeness: A search algorithm is said to be complete if
it guarantees to return a solution if at least any solution exists
for any random input.
Optimality: If a solution found for an algorithm is
guaranteed to be the best solution (lowest path cost) among
all other solutions, then such a solution for is said to be an
optimal solution.
Time Complexity: Time complexity is a measure of time for
an algorithm to complete its task.
Space Complexity: It is the maximum storage space
required at any point during the search, as the complexity of
the problem.
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6. Types of search algorithms
Based on the search problems we can classify
the search algorithms into uninformed
(Blind search) search and informed search
(Heuristic search) algorithms.
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8. Uninformed/Blind Search:
The uninformed search does not contain any domain
knowledge such as closeness, the location of the goal.
It operates in a brute-force way as it only includes
information about how to traverse the tree and how
to identify leaf and goal nodes.
Uninformed search applies a way in which search
tree is searched without any information about the
search space like initial state operators and test for
the goal, so it is also called blind search.
It examines each node of the tree until it achieves the
goal node.
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9. Cont….
It can be divided into five main types:
Breadth-first search
Uniform cost search
Depth-first search
Iterative deepening depth-first search
Bidirectional Search
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10. Informed Search
Informed search algorithms use domain knowledge. In an informed
search, problem information is available which can guide the
search.
Informed search strategies can find a solution more efficiently than
an uninformed search strategy. Informed search is also called a
Heuristic search.
A heuristic is a way which might not always be guaranteed for best
solutions but guaranteed to find a good solution in reasonable time.
Informed search can solve much complex problem which could not
be solved in another way.
An example of informed search algorithms is a traveling salesman
problem.
Greedy Search
A* Search
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