#AI #Chatbot #Artificial Intelligence #inbound sales

It is based on the GPT (Generative Pre-trained Transformer) architecture, which is a type of neural network model that has been pre-trained on a large corpus of text data. What good are the advancements of an AI-powered CRM if nobody on your team can figure out how to use it? It’s got a suite of products, including Marketing Hub, Sales Hub, Service Hub, and CMS Hub, that work together seamlessly. The system also automatically updates deal stages and assigns tasks based on predefined workflows, further reducing administrative overhead. First, group use cases into solution “packages” and choose two to four packages, each containing 5 to 10 use cases, to focus on (see Figure 2). If not, reflect, review, reevaluate, and retry using different AI strategies and techniques.

In an era dominated by technological advancements, AI is a groundbreaking force in content creation. Justin Robinsion-Prickett is a content writer from Los Angeles with over a decade of experience in the auto industry under his belt. When not working, he enjoys fencing, re-editing dialogue in old movies to remove articles, and playing with his two dogs James Westphal and Dr. Kenneth Noisewater. Most changes in our world are slow-paced – but it’s always wise to watch out for developments that can explode into exponential growth.

How Generative AI Can Empower B2B Marketers

AI can assign customer complaints to the right support group and then utilize statistical models to identify the best next action. Additionally, a digital marketing agency can include chatbots that processes onto their websites to provide customers with a rough idea of your services. Your company can gain an advantage by attracting customers, which is essential in a fast-growing sector.

Generative AI impacts sales by automating prospecting tasks, personalizing outreach strategies, improving writing efficiency and offering predictive analytics. So here’s an attempt to decipher the transformative power of generative AI and its profound implications for enhancing customer experience (CX) in the B2B realm. By automating repetitive tasks, providing valuable insights, and augmenting sales professionals’ capabilities, AI has the potential to boost productivity, creativity, and overall sales results. Pinpoint the areas of sales where generative AI can have the biggest effect, such as lead generation, opportunity qualification, and account management. Generative AI excels at processing huge volumes of data to uncover insights and make recommendations.

Insights

Chatbots also support sales by qualifying leads and aiding in the sales funnel, besides contributing to conversational commerce. With their 24/7 availability and ability to offer multilingual support, AI chatbots significantly improve customer experience, satisfaction, and retention. They streamline customer service, providing efficient, personalized support, and assist in agent training, shaping the future of customer engagement. Visual content is one of the most powerful AI-powered tools in digital marketing due to the fact that it captures attention by conveying messages effectively. Generative artificial intelligence has upraised multimedia and visual experiences to new levels by enabling digital marketers to create outstanding videos, visuals, and graphics with little effort.

• It is a web-based email marketing management service for small and medium businesses.
• While generative AI holds immense potential, it’s important to be aware of potential challenges.
• Generative AI helps businesses automate digital marketing while maintaining audience targeting.

Through this process, it learns patterns, styles, and structures, enabling it to create highly convincing content across different mediums. It’s a common misconception that generative AI has the potential to take over most sales processes. Comparable to the advent of phones, computers, and the internet, generative AI will become an integrated part of our workplaces, helping sales teams to target the best possible results. Instead of replacing sales processes, GenAI will augment them and turn into a virtual assistant and data analyst that you can call upon whenever you need help. Those who master the technology will have key knowledge to come out on top of their competitors, fostering better sales relationships with stakeholders and existing clients.

Expert recommendations

It replicates human creativity, producing content that often rivals human creations. Warmer.ai is your go-to solution for elevating outbound sales campaigns, providing SDRs with an ‘AI email writer’ to supercharge their outreach efforts and generate high-quality leads. With 22+ years of experience in Tech and business and founding multiple businesses, I have been fortunate to help 100′s leaders unlock the secrets of AI for business growth. Since ChatGPT’s 2022 launch, business leaders, and sellers who have adapted to using Generative AI and tools like chatGPT are experiencing transformative business & sales growth. As businesses embrace this transformative technology, they can expect to see increased productivity, reduced costs, and heightened customer loyalty, solidifying their position in the competitive market. Gen AI emerges as an empowering force, enriching human creativity by generating fresh ideas and concepts.

Generative AI provides businesses with a wealth of insights and data-driven recommendations, empowering sales teams to make faster and more informed decisions. By leveraging AI-generated predictions and trends, sales professionals can make strategic choices that align with both company objectives and customer expectations. This data-driven decision-making process minimizes risks and maximizes opportunities for growth.

Take Your Dealership Social Media Marketing to New Heights With Generative AI

The rapid adaptation of AI is also raising questions over the safe handling of user data and general cybersecurity concerns. Thus, some companies have adopted established principles that dictate how they use generative AI. Through advanced algorithms and deep learning, AI can automatically generate instructional content in various formats. It can synthesize voiceovers, select appropriate visuals, and even edit the video for a seamless viewing experience.

Furthermore, the integration of AI with customer relationship management (CRM) systems will provide sales professionals with invaluable insights and recommendations. AI-powered CRM platforms will automatically analyze customer interactions, providing real-time guidance on the most effective sales strategies and tactics. Its real-time image and video processing capabilities can help businesses create visually stunning content that resonates with their target audience.

We’ll look at how to best capitalize on its many advantages and choose the solution that best fits your business, helping you elevate sales to new heights. Sagefrog Lab is our unique in-house marketing research center for exploring and mastering new tools, concepts, and platforms as they arise. Sagefrog Lab allows us to stay at the forefront of technological innovation, identifying and rigorously testing new AI tools on ourselves before recommending and implementing them in client campaigns.

Additionally, generative AI for sales utilizes economic indicators, seasonality, and market trends to predict future sales trends. Unlike traditional sales methods, AI-augmented strategies leverage data-driven insights to enhance outcomes rather than relying solely on historical data and manual techniques. Marketing has evolved significantly in recent years, shifting from mass advertising to a focus on personalized, relevant content. To succeed today, businesses must deliver the right message to the right person at the right time, relying on data-driven insights. Regie is the innovative software that takes the hassle out of creating, testing, and analyzing personalized sales campaigns.

Optimized sales

The journey of effective B2B marketing doesn’t start with splurging on ads; it begins with crafting the right message and positioning your brand effectively. This is where AI proves to be a game changer, especially for startups and small businesses. This tool taps into LinkedIn’s vast professional network, using AI to recommend leads and decision-makers that align with your business needs.

Ethical considerations, data privacy, and the requirement for ongoing training and adaptation are crucial aspects that businesses need to navigate through. With the evolution of Generative AI, keeping yourself informed and implementing best practices will be vital to ensure a responsible and effective integration within the Salesforce CRM framework. Generative AI represents a revolutionary change in technology, which will result in a huge transformation in your business processes, workforce, skill requirements, and the equipment you use. Now is the time for your business to embrace Generative AI, in case it hasn’t already considered it. Automation uses technology to perform tasks that humans would otherwise do, while AI systems can learn, reason, and make decisions on their own.

Read more about The Role of AI in Marketing and Heights with Generative AI here.

How to use AI to build your business

Whether you have questions about our solutions, need technical support, or want to explore partnership opportunities, our team is ready to assist you. We can set up a pilot for your company, free of charge, and show you how BabelusAI can shorten to days or hours the supplier discovery process, which can sometimes take weeks or months. A technology that in the past was only accessible to researchers, IT developers, and mathematicians is now usable via a single search line. ChatGPT has created a “Google moment” that hints at the full extent of potential and possible use cases that come with generative AI.

Businesses can harness the capabilities of Shopify’s Magic to accelerate their content creation, enhance their store’s quality, manage time-consuming tasks effectively, and make more informed business decisions. NVIDIA is at the forefront of powering generative AI, which is impacting virtually every industry and unlocking a new frontier of opportunities. NVIDIA’s suite of cloud services, pre-trained foundation models, cutting-edge frameworks, optimized inference engines, and APIs are designed to bring intelligence to enterprise applications. Mixpanel Spark is a generative AI tool designed to simplify data analysis and accelerate decision-making.

Ways to Unlock the Power of Generative AI for Your Business

50% to 90% cost savings through efficient resource allocation, scalable on-demand operations, and economical deployment. 69% favorability boost with human feedback integration compared to standard results. In the future, we look forward to publishing more engaging articles exploring the role of AI in the practice of entrepreneurship, and we welcome all new submissions. We will release official guidance on the ethical use of generative AI technologies in publishing on EIX.org and FamilyBusiness.org in the near future. So we recommend you make a roadmap of your new implementations, issue the necessary guidelines, and determine the success metrics and people responsible for supervising the process.

Enhance efficiency across various business functions by making data-driven decisions and informed investments. Stay resilient with adaptive risk analysis, improving compliance, security, and adherence to industry standards. Incorporate adaptive scenarios to augment traditional risk management practices and advanced data monitoring. Once the generative AI solution passes all the necessary tests, it can be applied to tackle the intended real-world tasks. However, remember to constantly evaluate its performance and make upgrades to achieve the best possible output. The list of big names introducing generative AI into their business is a long one.

AI, ML, and Data Science services

This phase involves rapid prototyping and testing to validate ideas and gather feedback from alpha and beta user groups. Have multiple AI models running simultaneously or implement fail-safe mechanisms. Redundancy and continuous monitoring are key to ensuring reliability in generative AI systems. “When considering the use of an API, you need to consider the potential for slow response times or periods of unavailability. Understanding the finances allows you to plan your budget effectively and make informed decisions about resource allocation. By involving legal experts from the outset, you can establish a strong foundation of compliance, building trust with customers, stakeholders, and regulatory bodies.

Generative AI is a technology capable of generating new original content based on training from vast datasets. Combining them allows businesses to extract insights from their data using simple natural language bypassing the need for technically complex queries. Generative AI models can be used and integrated out of the box, significantly reducing AI projects’ complexity, effort, and cost. As a result, the complexity and scope of AI integration projects have decreased, making it possible to introduce AI-supported functionalities quickly and easily compared to previous projects. SAP has recognized this and increasingly offers AI-supported functional modules for solutions such as SAP S/4HANA or SAP Analytics Cloud.

Start by collecting relevant data from reliable sources, both internal and external to your organization. Once you have a clear vision of your business needs, it’s time to choose the right Generative AI platform or solution. There are various options available, each with its own strengths and capabilities. By suggesting creative ideas at scale https://www.metadialog.com/enterprise-ai-support-platform/ for new products, features, messaging, and campaigns, generative AI sparks innovation pipelines. Platforms like Anthropic can generate tailored sales proposals, contracts, presentations, and reports saving immense time. As AI continues to revolutionize industries, many non-AI native enterprises are grappling with how to harness its potential.

Generative AI models are being used for a variety of industry-specific enterprise use cases that range from simple customer service support and coaching to more complex tasks like medical imaging and drug discovery. The underlying technique works incredibly well, but training large language models makes vector search impractical for most businesses due to time requirements and processing costs. Instead of wiping out businesses and industries quickly, it will gradually eat away and weaken existing business models unless organizations create a strong foundation to counter and grow with it. Machine learning algorithms can analyze a user’s purchasing history and online behavior to improve product recommendations or generate custom content. Salespeople, meanwhile, can create personalized presentations, and marketers can hone their campaigns.

What can generative AI do for business applications?

Thanks to this, companies can now save a lot of resources and money and easily promote their business with the help of AI-generated content. Much of the success of gen AI integration into business processes depends on having the right blend of Integrate Generative AI into Your Business Easily business and technology expertise. If this expertise doesn’t fully exist within your organization, now is the time to seek support. As we’re already on the gen AI journey with many of our clients, we would be happy to help you get started.

Natural Language Processing Sentiment Analysis

Aspect analysis of feelings extracts the characteristics of the subject from the division of large data into blocks. The model evaluates a set of reviews about the product, highlighting the character of the subject and the phrases that are related to this characteristic. In this way, the analysis makes a general conclusion about the customer’s feedback. The aspect-based analysis is useful in that it helps identify specific topics that people are discussing.

Zero in on certain demographics to understand what works best and how you can improve. Businesses use these scores to identify customers as promoters, passives, or detractors. The goal is to identify overall customer experience, and find ways to elevate all customers to “promoter” level, where they, theoretically, will buy more, stay longer, and refer other customers. Social media and brand monitoring offer us immediate, unfiltered, and invaluable information on customer sentiment, but you can also put this analysis to work on surveys and customer support interactions.

Why Use Sentiment Analysis?

It is the one approach that truly digs into the text and delivers the goods. Instead of clearly defined rules – this type of sentiment analysis uses machine learning to figure out the gist of the message. The obvious disadvantage is a lack of ability to fine tune the algorithm. The only possible tuning is an adjustment of the threshold for “clearly positive” and “clearly negative”  sentiments for the specific use cases. Besides, usage costs for the Google Natural Language processing API are computed monthly based on which feature of the API is used, and how many text records are evaluated using those features. There are many ways to do sentiment analysis, but what Google offers is a kind of black box where you simply call an API and receive a predicted value.

Sentiment analysis is a method of contextual mining of reviews that extracts information that helps businesses to understand social reviews of their product or services. With advancements in machine learning technologies, we can analyze customer reviews and identify whether they are positive, negative, or neutral. Businesses use this information to know about customers’ concerns about their products or services and take appropriate decisions to improve their services. In this paper, we have described the sentiment analysis process including its definition, datasets, preprocessing, algorithms used, evaluating algorithms, and conclusion. Evaluation metrics such as precision, recall, accuracy, f1 score are used to check the performance of the algorithms used. This research-based survey is divided into different sections where each section describes a particular step of sentiment analysis.

Social Media Monitoring

The analysis revealed that 60% of comments were positive, 30% were neutral, and 10% were negative. Negative comments expressed dissatisfaction with the price, fit, or availability. As we can see that our model performed very well in classifying the sentiments, with an Accuracy score, Precision and  Recall of approx 96%. And the roc curve and confusion matrix are great as well which means that our model is able to classify the labels accurately, with fewer chances of error. It is a data visualization technique used to depict text in such a way that, the more frequent words appear enlarged as compared to less frequent words.

Sentiment analysis models can help you immediately identify these kinds of situations, so you can take action right away. Since humans express their thoughts and feelings more openly than ever before, sentiment analysis is fast becoming an essential tool to monitor and understand sentiment in all types of data. Alternatively, you could detect language in texts automatically with a language classifier, then train a custom sentiment analysis model to classify texts in the language of your choice. In this section, you will learn when and how to build a sentiment analysis model from scratch using TensorFlow. It is a simple python library that offers API access to different NLP tasks such as sentiment analysis, spelling correction, etc.

Nike, a leading sportswear brand, launched a new line of running shoes with the goal of reaching a younger audience. To understand user perception and assess the campaign’s effectiveness, Nike analyzed the sentiment of comments on its Instagram posts related to the new shoes. Now, we will check for custom input as well and let our model identify the sentiment of the input statement.

In the next section, we shall go through some of the most popular methods and packages. For example, researchers from India studied posts from X, formerly Twitter, related to the elections held in 2019. They performed sentiment analysis on the posts to understand the voters’ perception of the candidates. The results of this study were significantly correlated with the outcome; the candidate with more positive posts won the election.

Sentiment Analysis Using NLP: The Go-To Guide

And since machines learn from labeled data, sentiment analysis classifiers might not be as precise as other types of classifiers. Rule-based systems are very naive since they don’t take into account how words are combined in a sequence. Of course, more advanced processing techniques can be used, and new rules added to support new expressions and vocabulary. However, adding new rules may affect previous results, and the whole system can get very complex. Since rule-based systems often require fine-tuning and maintenance, they’ll also need regular investments.

Sentiment Analysis allows you to get inside your customers’ heads, tells you how they feel, and ultimately, provides actionable data that helps you serve them better. Since you’re shuffling the feature list, each run will give you different results. In fact, it’s important to shuffle the list to avoid accidentally grouping similarly classified reviews in the first quarter of the list. Note also that you’re able to filter the list of file IDs by specifying categories.

Applying Sentiment Analysis To Business:

Sentiment analysis, which enables companies to determine the emotional value of communications, is now going beyond text analysis to include audio and video. Understanding how your customers feel about each of these key areas can help you to reduce your churn rate. Research from Bain & Company has shown that increasing customer retention rates by as little as 5 percent can increase your profits by anywhere from 25 to 95 percent. Lettria’s API uses resources from psychology and the 8 primary emotions modelled in Putichik’s wheel of emotions (joy, sadness, fear, anger, attract, surprise, and anticipation). Language is constantly changing, especially on the internet where users are continually creating new abbreviations, acronyms, and using poor grammar and spelling.

Use language & statistical analyses to improve communication about circular economy. Emotion detection analysis identifies emotions rather than positivity and negativity. We will evaluate our model using various metrics such as Accuracy Score, Precision Score, Recall Score, Confusion Matrix and create a roc curve to visualize how our model performed.

It entails gathering data from multiple sources, cleaning and preparing it, choosing pertinent features, training and optimizing the sentiment analysis model, and assessing its performance using relevant metrics. Currently, transformers and other deep learning models seem to dominate the world of natural language processing. Sentiment analysis plays an important role in natural language processing (NLP). It is the confluence of human emotional understanding and machine learning technology. Sentiment analysis can be used on any kind of survey – quantitative and qualitative – and on customer support interactions, to understand the emotions and opinions of your customers. Tracking customer sentiment over time adds depth to help understand why NPS scores or sentiment toward individual aspects of your business may have changed.

Now we jump to something that anchors our text-based sentiment to TrustPilot’s earlier results. The predicted value is NEGATIVE, which is reasonable given the poor service. The Elasticsearch Relevance Engine (ESRE) gives developers the tools they need to build AI-powered search apps.

• What you mean by neutral, positive, or negative does matter when you train sentiment analysis models.
• This essentially means we need to build a pipeline of some sort that breaks down the problem into several pieces.
• Sentiment analysis provides valuable commercial insights, and its continuing advancement will improve our comprehension of human sentiment in textual data.
• Sentiment analysis is a vast topic, and it can be intimidating to get started.
• Several firms apply analysis to their customer care unit to better understand customer grievances and the need to improve certain PR aspects.

Read more about Sentiment Analysis NLP here.

What is Sentiment Analysis? Definition, Types, Algorithms

Organizations use this feedback to improve their products, services and customer experience. A proactive approach to incorporating sentiment analysis into product development can lead to improved customer loyalty and retention. It is essentially a multiclass text classification text where the given input text is classified into positive, neutral, or negative sentiment. The number of classes can vary according to the nature of the training dataset. It is not always obvious which word should be placed instead of the misspelled one.

Its value for businesses reflects the importance of emotion across all industries – customers are driven by feelings and respond best to businesses who understand them. You can create feature vectors and train sentiment analysis models using the python library Scikit-Learn. There are also some other libraries like NLTK , which is very useful for pre-processing of data (for example, removing stopwords) and also has its own pre-trained model for sentiment analysis. The goal of sentiment analysis is to classify the text based on the mood or mentality expressed in the text, which can be positive negative, or neutral.

How does the sentiment analysis process work?

Costs are a lot lower than building a custom-made sentiment analysis solution from scratch. This is why it’s necessary to extract all the entities or aspects in the sentence with assigned sentiment labels and only calculate the total polarity if needed. Picture when authors talk about different people, products, or companies (or aspects of them) in an article or review.

That makes all the difference and takes the lid off the unexpressed opinion. In this article, we will look at what is sentiment analysis and how it can be used for the benefit of your company. I believe that someday people will include this one in their all-time top 10’s. Not now, but in the far future.”The overall sentiment of the document as judged by Google is positive, the score equals 0.3. Expert.ai’s Natural Language Understanding capabilities incorporate sentiment analysis to solve challenges in a variety of industries; one example is in the financial realm.

Getting Started With NLTK

By classifying text as positive, negative, or neutral, sentiment analysis aids in understanding customer sentiments, improving brand reputation, and making informed business decisions. Sentiment analysis is a subset of natural language processing (NLP) that uses machine learning to analyze and classify the emotional tone of text data. Basic models primarily focus on positive, negative, and neutral classification but may also account for the underlying emotions of the speaker (pleasure, anger, indignation), as well as intentions to buy. Python is a popular programming language for natural language processing (NLP) tasks, including sentiment analysis. Sentiment analysis is the process of determining the emotional tone behind a text.

I know how you feel, but let’s use a real-world example to make things a bit clearer. The emotional value of a statement is determined by using the following graded analysis. Filling in your return form was really time-consuming, but the refund was handled very quickly. This process means that the more data you feed through your NLP the more accurate it becomes. With each new analysis allowing it to build a more complete knowledge bank that helps it to make more accurate and complete analysis.

What are the challenges in sentiment analysis?

Organizations use sentiment analysis as a metric to strategize, plan, and implement PR strategies. Several firms apply analysis to their customer care unit to better understand customer grievances and the need to improve certain PR aspects. For example, industry and market trends can provide sales leads through sentiment analysis. Therefore, the data set you have labeled is key to training the model to produce accurate results. The model will receive different patterns of data in the text and be able to predict sentiments for the text you provide.

Natural Language Processing (NLP) allows researchers to gather such data and analyze it to glean the underlying meaning of such writings. The field of sentiment analysis—applied to many other domains—depends heavily on techniques utilized by NLP. This work will look into various prevalent theories underlying the NLP field and how they can be leveraged to gather users’ sentiments on social media.

You can use sentiment analysis to conduct market research and perform competitor analysis. Brand managers can gain valuable competitive intelligence by analyzing their competitor’s social media posts, forums, news articles, review sites, and more. This analysis can help them identify their competitor’s strengths, weaknesses, and customer pain points, giving them opportunities for differentiation and improvement.

When it comes to brand reputation management, sentiment analysis can be used for brand monitoring to analyze the web and social media buzz about a product, a service, a brand, or a marketing campaign. Right now, the users of the Brand24 app are using the best technology possible to evaluate the sentiment around their brand, products, and services. Sentiment analysis is the process of analyzing online text to determine the emotional tone they carry.

Benefits of sentiment analysis

Now, let’s get our hands dirty by implementing Sentiment Analysis, which will predict the sentiment of a given statement. As we humans communicate with each other in a way that we call Natural Language which is easy for us to interpret but it’s much more complicated and messy if we really look into it. Sentiment Analysis, as the name suggests, it means to identify the view or emotion behind a situation.

Uncover trends just as they emerge, or follow long-term market leanings through analysis of formal market reports and business journals. You can analyze online reviews of your products and compare them to your competition. Find out what aspects of the product performed most negatively and use it to your advantage. We already looked at how we can use sentiment analysis in terms of the broader VoC, so now we’ll dial in on customer service teams. You can use it on incoming surveys and support tickets to detect customers who are ‘strongly negative’ and target them immediately to improve their service.

Twitter Sentiment Classification using Distant Supervision

Have a little fun tweaking is_positive() to see if you can increase the accuracy. In this case, is_positive() uses only the positivity of the compound score to make the call. You can choose any combination of VADER scores to tweak the classification to your needs. You don’t even have to create the frequency distribution, as it’s already a property of the collocation finder instance. Another powerful feature of NLTK is its ability to quickly find collocations with simple function calls.

Commercial and publicly available tools often have big databases, but tend to be very generic, not specific to narrow industry domains. Companies use sentiment analysis to evaluate customer messages, call center interactions, online reviews, social media posts, and other content. Sentiment analysis can track changes in attitudes towards companies, products, or services, or individual features of those products or services.

Sentiment analysis offers a vast set of data, making it an excellent addition to any type of market research. Now let’s detect who is talking about Marvel in a positive and negative way. With a Brand24 tool, I detected that about 123k of those mentions are positive, 9k are negative, and the rest is neutral. Sure, you can try to research and analyze mentions about your business on your own, but it will take lots of your time and energy. The NVIDIA RAPIDS™ suite of software libraries, built on CUDA-X AI, gives you the freedom to execute end-to-end data science and analytics pipelines entirely on GPUs.

However, sentiment analysis faces challenges, such as irony and sarcasm, fake reviews, and misspellings, and how these challenges make the sentiment analysis process more challenging. If you can spend time writing, testing, and supporting your service, try going with pre-trained models from spaCy of HuggingFace. They provide decent performance but require more time before you can use them. Now that you know what sentiment analysis is and its use cases, let us understand how it works. First, we will go over the different types of sentiment analysis and then learn how real-life solutions are built.

Sometimes the message does not contain the explicit sentiment, sometimes the implicit sentiment is not what it seems. The harder task is to determine whether the message is objective or subjective. Discover new opportunities for your travel business, ask about the integration of certain technology, and of course – help others by sharing your experience. IncluIT becomes Avenga LATAM, a dynamic software development company revolutionizing the US and Latin American market with cutting-edge solutions.

• In Brazil, federal public spending rose by 156% from 2007 to 2015, while satisfaction with public services steadily decreased.
• Aside from that, machine learning models can use rules as input features.
• Lettria’s API uses resources from psychology and the 8 primary emotions modelled in Putichik’s wheel of emotions (joy, sadness, fear, anger, attract, surprise, and anticipation).
• First aid for mental health is not very popular and developed as compared to physical health.
• Because neural nets are created from large numbers of identical neurons, they’re highly parallel by nature.

Read more about Sentiment Analysis NLP here.