The need for accurate and actionable information is becoming more and more important for financial businesses in today’s data-driven environment. The sheer volume and complexity of financial data, however, can pose significant challenges. It is possible to annotate and label data using a variety of services. Financial data can provide valuable insights, minimize risks, and assist businesses in making informed decisions by obtaining structured, annotated, and labeled data. This article explores the significance of using financial data annotation and labeling services to enhance the efficiency and effectiveness of business processes is the purpose of this article.

Assuring the accuracy and consistency of data

There can be inconsistencies and errors in financial data, resulting in flawed decisions. Data annotation and labeling are essential for ensuring accuracy and consistency. This service analyzes and labels financial data points such as transactions, trends, and investment portfolios to ensure data integrity and reduce ambiguity. In order to make informed business decisions, you must have reliable and consistent data. The risk of inaccurate or incomplete information being presented to clients will be reduced as a result. Therefore, it becomes obligatory to seek the help of professional financial data annotation and labeling services providers.

Increasing the ability to interpret and analyze data

It can be difficult and overwhelming to interpret raw financial data. Annotations and labeling can facilitate the analysis and interpretation of data by providing context and categorization. The financial data annotation and labeling services will provide certain details about the type of transaction, the sector classification, the level of risk, and the characteristics of the client in accordance with the annotation. Business owners can better understand market dynamics and operations by improving their understanding of data interpretation. The information provided by this resource can be used by businesses to make strategic decisions, optimize their investment portfolios, and identify new growth opportunities.

Complying with regulatory requirements

Businesses operating in the financial sector must comply with financial regulations. Regulatory compliance can be achieved by identifying sensitive data, such as personally identifiable information (PII), transactional data, and fraud indicators, in an accurate manner through data annotation and labeling services. It is important that financial organizations label their data correctly in order to implement robust data privacy measures, to comply with regulatory requirements, and to minimize the risk of non-compliance. This results in the protection of sensitive data as well as the building of trust within the industry and among customers.

Assessing and managing risks more effectively

In order to make informed financial decisions, risk assessment and management are essential. By providing granular and structured data, data annotation and labeling services greatly contribute to this process. Data points can be tagged with risk indicators, historical trends, and market conditions to provide businesses with a comprehensive view of their risk exposure. As a result, they can accurately assess and quantify risks, develop risk mitigation strategies, and make informed decisions regarding protecting their assets and investments. Financial businesses can benefit from better managing risks by minimizing losses, maximizing returns, and navigating market fluctuations more effectively. Thus, the role of financial data annotation and labeling services is huge.

Machine learning and predictive analytics enabled

The financial sector is undergoing a transformation due to predictive analytics and machine learning algorithms. In order for them to be effective, they must be trained on high-quality labeled data. Annotation and labeling services are crucial for preparing labeled datasets for accurate predictions and insights. These services allow financial businesses to develop robust predictive models by annotating historical data, market variables, and other relevant factors. A number of functions can be achieved using this technology, including predicting market trends, detecting anomalies, identifying investment opportunities, automating decision-making processes, and improving operational efficiency.

Assisting in the preparation of financial reports and audits

Transparency and compliance require accurate financial reporting and auditing. In order to prepare reliable financial reports and facilitate auditing processes, data annotation and labeling services are beneficial to financial businesses. These services ensure the accuracy and integrity of financial data by annotating it with appropriate labels and categorizing it. This facilitates a seamless analysis of financial data, the evaluation of performance, and compliance with regulatory requirements. A properly labeled financial report can help financial business owners instill confidence in their investors, shareholders, and regulators and foster strong relationships with them.

Integrating and collaborating with data in a more efficient manner

It is not uncommon for financial businesses to operate with data from a variety of sources, including internal systems, third-party providers, and data from external markets. As a result of data annotation and labeling services, data integration and collaboration across these diverse sources can be streamlined. Data can be analyzed and shared more effectively if it is annotated and labeled consistently. Collaboration between different teams and departments is facilitated, cross-functional insights are promoted, and effective decision-making is enabled through data-driven decision-making.

Innovating and adapting

The ability to innovate and adapt is a key component to staying competitive in today’s fast-paced financial environment. As a result of annotations and labels being applied to data, advanced analytical models, algorithms, and tools can be developed based on structured and annotated data. As a result of this approach, financial organizations are able to identify emerging trends, identify new patterns, as well as adjust their strategies as necessary. In order to gain a competitive advantage and capitalize on emerging opportunities, businesses are turning to innovative technologies, such as artificial intelligence and machine learning.

Finally, a few thoughts.

When it comes to making financial decisions, accurate and reliable data is of utmost importance. By leveraging data annotation and labeling services, financial businesses can maximize the value of their data. By ensuring accuracy and consistency, providing predictive analytics, and facilitating regulatory compliance, they play a critical role in unlocking the potential of financial data. It is possible to improve the decision-making process of a financial company through the implementation of data annotations and labeling services, resulting in better-informed decisions, reduced risks, increased operational efficiency, and keeping up with the ever-changing world of finance through the implementation of these services. Using labeled data, you can make better and more successful financial decisions.

Featured Image Credit: Provided by the Author; Pexels; Thank you!

The post Unlocking the Power of Financial Data: How Data Annotation Enhance Decision-Making appeared first on ReadWrite.

Natural Language Processing (NLP), which encompasses areas such as linguistics, computer science, and artificial intelligence, has been developed to understand better and process human language. In simple terms, it refers to the technology that allows machines to understand human speech.

NLP is used to develop systems that can understand human language in various contexts, including the syntax, semantics, and context of the language. As a result, computers can recognize speech, understand written text, and translate between languages.

NLP is a Deep Learning Technology

With the advancement of deep learning technologies, machine learning, and NLP data labeling techniques, NLP has become increasingly popular. NLP algorithms can analyze large datasets to detect patterns in the text and extract meaningful information. By using this technology, computers can now process large amounts of data, including emails, texts, and tweets, automatically.

In addition to creating natural language text, NLP can also generate structured text for various purposes. To accomplish the structured text, algorithms are used to generate text with the same meaning as the input. The process can be used to write summaries and generate responses to customer inquiries, among other applications.

An Overview of NLP’s Utility

The field of natural language processing deals with the interpretation and manipulation of natural languages and can therefore be used for a variety of language-inclined applications. A wide range of applications of natural language processing can be found in many fields, including speech recognition and natural language understanding. NLP generates and extracts information, machine translation, summarization, and dialogue systems. The system can also be used for analyzing sentiment and generating automatic summaries.

With improved NLP data labeling methods in practice, NLP is becoming more popular in various powerful AI applications. Besides creating effective communication between machines and humans, NLP can also process and interpret words and sentences. Text analysis, machine translation, voice recognition, and natural language generation are just some of the use cases of NLP technology. NLP can be used to solve complex problems in a wide range of industries, including healthcare, education, finance, and marketing.

Using NLP, machines can identify large amounts of data accurately and process them efficiently.

It helps machines to develop more sophisticated and advanced applications of artificial intelligence by providing a better understanding of human language. A natural language processing system provides machines with a more effective means of interacting with humans and gaining a deeper understanding of their thoughts.

NLP Use Cases

In diverse industries, natural language processing applications are being developed that automate tasks that were previously performed manually. Throughout the years, we will see more and more applications of NLP technology as it continues to advance.

Presented here is a practical guide to exploring the capabilities and use cases of natural language processing (NLP) technology and determining its suitability for a broad range of applications.

NLP Use Cases Based on Its Practical Applications

1. NLP for Automated Chatbots

In almost every industry, chatbots are being used to provide customers with more convenient, personalized experiences, and NLP plays a key role in how chatbot systems work. The automated systems based on NLP data labeling enable computers to recognize and interpret human language. This leads to the development of chatbot applications that can be integrated into online platforms for comprehending users’ queries and responding to them with appropriate replies.

NLP-enabled chatbots can offer more personalized responses as they understand the context of conversations and can respond appropriately. Chatbots using NLP can also identify relevant terms and understand complex language, making them more efficient at responding accurately. A chatbot using NLP can also learn from the interactions of its users and provide better services over the course of time based on that learning.

2. NLP for Text Classification

An NLP-based approach for text classification involves extracting meaningful information from text data and categorizing it according to different groups or labels. NLP techniques such as tokenization, part-of-speech tagging, named entity recognition, and sentiment analysis are utilized to accomplish this.

Using the above techniques, the text can be classified according to its topic, sentiment, and intent by identifying the important aspects. There are many possible applications for this approach, such as document classification, spam filtering, document summarization, topic extraction, and document summarization.

3. NLP for Machine Translation

In addition to helping machines analyze, interpret, and process natural languages, Natural Language Processing also enables machine translation. The use of NLP is a primary method for building machine translation systems for translating text between languages. By analyzing source texts, identifying their meaning, and generating translations in the target language that convey the same meaning, machine translation uses natural language processing.

An NLP-based machine translation system captures linguistic patterns and semantic data from large amounts of bilingual data using sophisticated algorithms. A word, phrase, or other elements in the source language is detected by the algorithm, and then a word, phrase, or element in the target language that has the same meaning is detected by the algorithm. The translation accuracy of machine translation systems can be improved by leveraging context and other information, including sentence structure and syntax.

4. NLP for Named Entity Recognition

Natural language processing (NLP) incorporates named entity recognition (NER) for identifying and classifying named entities within texts, such as people, organizations, places, dates, etc. The NER is an important part of many NLP applications, including machine translation, text summarization, and question-answer. It involves classifying words in a text into different categories, such as people, organizations, places, dates, etc.

The NER process recognizes and identifies text entities using techniques such as machine learning, deep learning, and rule-based systems. Using machine learning-based systems involves learning with supervised learning models and then classifying entities in a text after learning from appropriately labeled NLP data. Using support vector machines (SVMs), for example, a machine learning-based system might be able to construct a classification system for entities in a text based on a set of labeled data.

5. NLP for Natural Language Generation

As part of natural language processing (NLP), Natural Language Generation (NLG) generates natural language based on structured data, such as databases or semantic graphs. Automated NLG systems produce human-readable text, such as articles, reports, and summaries, to automate the production of documents.

NLG involves analyzing, interpreting, and formatting input data so that it is readable by humans by generating text that accurately conveys both the data and its meaning. The meaning of the input data can also be understood by NLG systems using Natural Language Understanding (NLU) techniques.

6. NLP for Question Answering

A question-answering (QA) system analyzes a user’s question and provides a relevant answer, which is a type of natural language processing (NLP) task. Natural language understanding, sentiment analysis, information retrieval, and machine learning are some of the facets of NLP systems that are used to accomplish this task.

In natural language understanding (NLU), context and intent are identified by analyzing the language used by the user in their question. As a result, the system can determine which method is most appropriate to respond to the user’s inquiry. It is necessary for the system to be capable of recognizing and interpreting the words, phrases, and grammar used in the question to accomplish this goal.

A question-answering system is an approach to retrieving relevant information from a data repository. Based on the available data, the system can provide the most accurate response. Over time, machine learning based on NLP improves the accuracy of the question-answering system. In this way, the QA system becomes more reliable and smarter as it receives more data.

7. NLP for Word Sense Disambiguation

The use of NLP can also lead to the creation of a system for word sense disambiguation. WSD (Word Sense Disambiguation) describes the process of determining what a word means in a given context using Natural Language Processing (NLP).

This system assigns the correct meaning to words with multiple meanings in an input sentence. For this, data can be gathered from a variety of sources, including web corpora, dictionaries, and thesauri, in order to train this system. When the system has been trained, it can identify the correct sense of a word in a given context with great accuracy.

There are many ways to use NLP for Word Sense Disambiguation, like supervised and unsupervised machine learning, lexical databases, semantic networks, and statistics. The supervised method involves labeling NLP data to train a model to identify the correct sense of a given word — while the unsupervised method uses unlabeled data and algorithmic parameters to identify possible senses.

Word meanings can be determined by lexical databases that store linguistic information. With semantic networks, a word’s context can be determined by the relationship between words. The final step in the process is to use statistical methods to identify a word’s most likely meaning by analyzing text patterns.

8. NLP for Text Summarization

A text summarization technique uses Natural Language Processing (NLP) to distill a piece of text into its main points. A document can be compressed into a shorter and more concise form by identifying the most important information. Text summaries are generated by natural language processing techniques like natural language understanding (NLU), machine learning, and deep learning. Machine learning and deep learning help to generate the summary by identifying the key topics and entities in the text.

In text summarization, NLP also assists in identifying the main points and arguments in the text and how they relate to one another. A natural language processing system for text summarization can produce summaries from long texts, including articles in news magazines, legal and technical documents, and medical records. As well as identifying key topics and classifying text, text summarization can be used to classify texts.

9. NLP for Sentiment Analysis

The process of sentiment analysis consists of analyzing the emotions expressed in a question. It allows the system to determine the user’s emotional reaction to the question, which can help contextualize the response. In NLP (Natural Language Processing), human language is analyzed, understood, and interpreted by artificial intelligence.

Text clustering, sentiment analysis, and text classification are some of the tasks it can perform. As part of NLP, sentiment analysis determines a speaker’s or writer’s attitude toward a topic or a broader context. News articles, social media, and customer reviews are the most common forms of text to be analyzed and detected.

Text classification, clustering, and sentiment analysis are some of the techniques used by NLP to process large quantities of text data. In text classification, documents are assigned labels based on their content. The text clustering method groups documents whose content is similar. To improve their products and services, businesses use sentiment analysis to understand the sentiment of their customers. As well as gauging public opinion, it is also used to measure the popularity of a topic or event.

10. NLP for Speech Recognition

With NLP, it is possible to design systems that can recognize and comprehend spoken language, as well as respond appropriately — we call this Speech Recognition. The NLP technologies, such as Automatic Speech Recognition (ASR) and Text-to-Speech (TTS), are used for Speech Recognition.

With ASR, spoken words can be recognized and understood. Algorithms determine the language and meaning of words spoken by the speaker. A text-to-speech (TTS) technology generates speech from text, i.e., the program generates audio output from text input.

A system can recognize words, phrases, and concepts based on NLP algorithms, which enable it to interpret and understand natural language. A computer model can be used to determine the context and meaning of a word, phrase, or sentence based on its context and meaning.

The system can then respond appropriately based on the user’s intent. An efficient and natural approach to speech recognition is achieved by combining NLP data labeling-based algorithms, ML models, ASR, and TTS. The use of speech recognition systems can be used as a means of controlling virtual assistants, robots, and home automation systems with voice commands.

11. NLP for Entity Linking

Entity Linking is a process for identifying and linking entities within a text document. NLP is critical in information retrieval (IR) regarding the appropriate linking of entities. An entity can be linked in a text document to an entity database, such as a person, location, company, organization, or product. As a result of this process, search engines can understand the text better, and search results are improved as well.

Using natural language to link entities is a challenging undertaking because of its complexity. NLP techniques are employed to identify and extract entities from the text to perform precise entity linking. In these techniques, named entities are recognized, part-of-speech tags are assigned, and terms are extracted. It is then possible to link these entities with external databases such as Wikipedia, Freebase, and DBpedia, among others, once they have been identified.

It is becoming increasingly important for organizations to use natural language processing for entity linking as they strive to understand their data better. Many text analytics and search engine optimization (SEO) applications use it to rank the most relevant results based on the user’s query. In addition to improving search engine results, NLP for Entity Linking can also help organizations gain insights from their data through a better understanding of the text.

Final Thought

NLP is an emerging field of artificial intelligence and has considerable potential in the future. This technology has the potential to revolutionize our interactions with machines and automate processes to make them more efficient and convenient. Natural Language Processing (NLP) could one day generate and understand natural language automatically, revolutionizing human-machine interaction.

Using advanced NLP data labeling techniques and innovations in AI, machine learning models can be created, and intelligent decision-making systems can be developed, which makes NLP increasingly useful. In addition to understanding human language in real time, NLP can be used to develop interactive machines that work as an integrated communication grid between humans and machines. In conclusion, it’s anticipated that NLP will play a significant role in AI technology for years to come.

The post 11 NLP Use Cases: Putting the Language Comprehension Tech to Work appeared first on ReadWrite.

Digital space is highly influenced by user-generated content — as we all can see an unimaginable volume of text, images, and video shared on multiple social media and other online platforms/websites. With numerous social media platforms, forums, websites, and other online platforms in access, businesses & brands can’t keep track of all the content users share online. 

Keeping tabs on social influences on brand perception and complying with official regulations are essential to maintaining a safe and trustworthy environment. Objectives that aim to create a safe & healthy online environment can be achieved effectively through content moderation, i.e., the process of screening, monitoring, and labeling user-generated content in compliance with platform-specific rules.

Individuals’ online opinions published on social media channels, forums, and media publishing sites have become a substantial source to measure the credibility of businesses, institutions, commercial ventures, polls & political agendas, etc.

What is Content Moderation?

The content moderation process involves screening users’ posts for inappropriate text, images, or videos that, in any sense, are relatable to the platform or have been restricted by the forum or the law of the land. A set of rules is used to monitor content as part of the process. Any content that does not comply with the guidelines is double-checked for inconsistencies, i.e., if the content is appropriate to be published on the site/platform. If any user-generated content is found inconsistent to be posted or published on the site, it is flagged and removed from the forum.

There are various reasons why people may be violent, offensive, extremist, nudist, or otherwise may spread hate speech and infringe on copyrights. The content moderation program ensures that users are safe while using the platform and tend to promote businesses’ credibility by upholding brands’ trust. Platforms such as social media, dating applications and websites, marketplaces, and forums use content moderation to keep content safe.

Exactly Why Does Content Moderation Matter?

User-generated content platforms struggle to keep up with inappropriate and offensive text, images, and videos due to the sheer amount of content created every second. Therefore, it is paramount to ensure that your brand’s website adheres to your standards, protects your clients, and maintains your reputation through content moderation.

The digital assets, e.g., business websites, social media, forums, and other online platforms, need to be under strict scrutiny to ascertain that the content posted thereon is in line with the standards set out by media and the various platforms. In any case of violation, the content must be accurately moderated, i.e., flagged and removed from the site. Content moderation here serves the purpose – it can be summed up to be an intelligent data management practice that allows platforms to be free of any inappropriate content, i.e., the content that in any way is abusive, explicit, or unsuitable for online publishing.

Content Moderation Types

Content moderation has different types based on the types of user-generated content posted on the sites and the specifics of the user base. The sensitivity of the content, the platform that the content has been posted on, and the intent behind the user content are some critical factors for determining the content moderation practices. Content moderation can be done in several ways. Here are the five significant types of content moderation techniques that have been in practice for some time:

1 Automated Moderation

Technology helps radically simplify, ease, and speed up the moderating process today. The algorithms powered by artificial intelligence analyze text and visuals in a fraction of the time it would take people to do it. Most importantly, they don’t suffer psychological trauma because they are not subjected to unsuitable content.

Text can be screened for problematic keywords using automated moderation. More advanced systems can also detect conversational patterns and relationship analysis.

AI-powered image annotation and recognition tools like Imagga offer a highly viable solution for monitoring images, videos, and live streams. Various threshold levels and types of sensitive imagery can be controlled through such solutions.

While tech-powered moderation is becoming more precise and practical, it cannot entirely eliminate the need for manual content review, especially when the appropriateness of the content is the genuine concern. That’s why automated moderation still combines technology and human moderation.

2 Pre-Moderation

Content moderation this way is the most extensive method where every piece of content is reviewed before being published. The text, image, or video content meant to be published online is first sent to the review queue to analyze it for suitability for online posting. Content that the content moderator has explicitly approved goes live only after the necessary moderation.

While this is the safest approach to barricade harmful content, the process is slow and not applicable to the rapid online world. However, platforms requiring strict content compliance measures can implement the pre-moderation method for fixing the content. A typical example is platforms for children where the security of the users comes first.

3 Post-Moderation

Generally, content is screened through post-moderation. The posts can be made whenever the user wants, but they are queued up for moderation before they are published. Whenever an item is flagged for removal, it is removed to ensure the safety of all users.

The platforms aim to reduce the amount of time that inappropriate content remains online by speeding up review time. Today, many digital businesses prefer post-moderation even though it is less secure than pre-moderation.

4 Reactive Moderation

As part of reactive moderation, users are asked to flag content they think is inappropriate or breaches the terms of service of your platform. Depending on the situation, it may be a good solution.

To optimize results, reactive moderation should be used in conjunction with post-moderation or as a standalone method. In this case, you get a double safety net, as users can flag content even after it has passed the whole moderation process.

5 Distributed Moderation

Online communities are entirely responsible for reviewing and removing content in this type of moderation. Contents are rated by users according to their compliance with platform guidelines. However, because of its reputational and legal risks, this method is seldom used by brands.

How Content Moderation Tools Work to Label Content

Setting clear guidelines about inappropriate content is the first step toward using content moderation on your platform. By doing this, the content moderators can identify which content needs to be removed. Any text, i.e., social media posts, users’ comments, customers’ reviews on a business page, or any other user-generated content, is moderated with labels put on them.

Alongside the type of content that needs to be moderated, i.e., checked, flagged, and deleted, the moderation limit has to be set based on the level of sensitivity, impact, and targeted point of the content. What more to check is the part of the content with a higher degree of inappropriateness that needs more work and attention during content moderation.

How Content Moderation Tools Work

There are various types of undesirable content on the internet, ranging from seemingly innocent photos of pornographic characters, whether real or animated, to unacceptable racial digs. It is, therefore, wise to use a content moderation tool that can detect such content on digital platforms. The content moderation companies, e.g., Cogito, Anolytics, and other content moderation experts work with a hybrid moderation approach that involves both human-in-the-loop and AI-based moderation tools.

While the manual approach promises the accuracy of the moderated content, the moderation tools ensure the fast-paced output of the moderated content. The AI-based content moderation tools are fed with abundant training data that enable them to identify the characters and characteristics of text, images, audio, and video content posted by users on online platforms. In addition, the moderation tools are trained to analyze sentiments, recognize intent, detect faces, identify figures with nudity & obscenity, and appropriately mark them with labels after that.

Content Types That are Moderated

Digital content is made up of 4 different categories, e.g., text, images, audio, and video. These categories of content are moderated depending on the moderation requirements.

1. Text

The text shares the central part of the digital content — it is everywhere and accompanies all visual content. This is why all platforms with user-generated content should have the privilege of moderating text. Most of the text-based content on the digital platforms consists

• Blogs, Articles, and other similar forms of lengthy posts
• Social media discussions
• Comments/feedbacks/product reviews/complaints
• Job board postings
• Forum posts

Moderating user-generated text can be quite a challenge. Picking the offensive text and then measuring its sensitivity in terms of abuse, offensiveness, vulgarity, or any other obscene & unacceptable nature demands a deep understanding of content moderation in line with the law and platform-specific rules and regulations.

2. Images

The process of moderating visual content is not as complicated as moderating text, but you must have clear guidelines and thresholds to help you avoid making mistakes. You must also consider cultural sensitivities and differences before you act to moderate images, so you must know your user base’s specific character and their cultural setting.

Visual content-based platforms like Pinterest, Instagram, Facebook, and likewise are well exposed to the complexities around the image review process, particularly of the large size. As a result, there is a significant risk involved with the job of content moderators when it comes to being exposed to deeply disturbing visuals.

3. Video

Among the ubiquitous forms of content today, video is difficult to moderate. For example, a single disturbing scene may not be enough to remove the entire video file, but the whole file should still be screened. Though video content moderation is similar to image content moderation as it is done frame-by-frame, the number of frames in large-size videos turns out to be too much hard work.

Video content moderation can be complicated when they consist of subtitles and titles within. Therefore, before proceeding with video content moderation, one must ensure the complexity of moderation by analyzing the video to see if there has been any title or subtitles integrated into the video.

Content moderator roles and responsibilities

Content moderators review batches of articles – whether they’re textual or visual – and mark items that don’t comply with a platform’s guidelines. Unfortunately, this means a person must manually review each item, assessing its appropriateness and thoroughly reviewing it. This is often relatively slow — and dangerous — if an automatic pre-screening does not assist the moderator.

Manual content moderation is a hassle that no one can escape today. Moderators’ psychological well-being and psychological health are at risk. Any content that appears disturbing, violent, explicit, or unacceptable is moderated accordingly based on the sensitivity level.

The most challenging part of content moderation is identifying has been taken over by multifaceted content moderation solutions. Some content moderation companies can take care of any type and form of digital content.

Content Moderation Solutions

Businesses that rely heavily on user-generated content have immense potential to take advantage of AI-based content moderation tools. The moderation tools are integrated with the automated system to identify the unacceptable content and process it further with appropriate labels. While human review is still necessary for many situations, technology offers effective and safe ways to speed up content moderation and make it safer for content moderators.

The moderation process can be scalably and efficiently optimized through hybrid models. The content moderation process has now been maneuvered with modern moderation tools that provide professionals with ease of identifying unacceptable content and further moderating it in line with the legal and platform-centric requirements. Having a content moderation expert with industry-specific expertise is the key to attaining accuracy and timely accomplishment of the moderation work.

Final thoughts

Human moderators can be instructed on what content to discard as inappropriate, or AI platforms can perform precise content moderation automatically based on data collected from AI platforms. Manual and automated content moderations are sometimes used together to achieve faster and better results. The content moderation experts in the industry, e.g., Cogito, Anolytics , etc., can hand out their expertise to set your online image right with content moderation services.

The post A Guide to Content Moderation, Types, and Tools appeared first on ReadWrite.