October 28, 2025

41 min

Elena Simciuc

Topic

1. Introduction
2. Natural Language Processing (NLP)
3. Predictive Analytics and Machine Learning (ML)
4. Generative AI
5. Computer Vision (CV)
6. Conversational AI
7. Sentiment Analysis
8. Sources

Article navigation

In the modern era of digital transformation, artificial intelligence (AI) has ceased to be a futuristic concept and has become a powerful tool that fundamentally reshapes business processes across all industries. The sales domain, traditionally reliant on human relationships and intuition, is now undergoing a revolution driven by the adoption of AI technologies. These technologies make it possible to automate routine tasks, extract deep analytical insights from massive volumes of data, and personalize customer interactions at an unprecedented level. For companies seeking to remain competitive and improve efficiency, understanding and applying AI is no longer merely an advantage but a necessity. As noted by Bain & Company, companies that implement AI can double the time spent on sales activities and achieve an increase in win rates of 30% or more.

The purpose of this report is to provide an in-depth study of six key types of artificial intelligence technologies used in sales: natural language processing (NLP), predictive analytics and machine learning (ML), generative AI, computer vision, conversational AI, and sentiment analysis. The report is designed for a dual audience: sales managers who need to understand the strategic opportunities and business benefits of these technologies, and technical specialists who are interested in the underlying principles, algorithms, and models. For each technology, we examine its core concepts, specific sales use cases, potential benefits, as well as existing limitations and risks. This comprehensive analysis is intended to serve as a guide for making informed decisions about AI adoption and maximizing its contribution to sales growth.

Introduction

Natural Language Processing (NLP)

Natural language processing is a field of artificial intelligence that enables computers to understand, interpret, and generate human language, both written and spoken. In the context of sales, NLP technologies are used to analyze unstructured data such as emails, call recordings, chat messages, and social media interactions, transforming them into valuable and actionable insights. This makes it possible to automate many processes and gain a deeper understanding of customer needs, directly impacting sales effectiveness.

At the core of NLP lies a complex, multi-stage process of transforming human language into a form that machines can understand. The first step is tokenization, in which text is broken down into smaller units—words or sentences (tokens). This is followed by part-of-speech (POS) tagging, where each token is assigned a grammatical category (noun, verb, adjective), helping to identify the syntactic structure of a sentence. Another important component is named entity recognition (NER), which identifies specific objects in text such as people’s names, organization names, dates, and geographic locations. These basic operations allow unstructured text to be structured.

For deeper semantic understanding, NLP systems use semantic analysis and vector representations of words (word embeddings), which represent words as vectors in a multidimensional space that reflects their contextual relationships. Modern NLP systems heavily rely on machine learning and deep learning models, including neural networks, to perform complex tasks such as text classification, sentiment analysis, and intent detection.

The application of NLP in sales covers a wide range of tasks aimed at improving efficiency and personalization. One of the key areas is lead scoring and qualification. NLP algorithms analyze the content of communications with potential customers, identifying keywords, questions, and expressions that indicate purchasing intent. This allows leads to be automatically prioritized, directing sales managers’ efforts toward the most promising prospects. For example, a system can determine that a customer using phrases such as “price comparison” or “delivery terms” is at a later stage of the sales funnel.

Another important application is conversation analysis. NLP tools transcribe recordings of calls and meetings and then analyze their content. This makes it possible to identify successful speech patterns, frequently occurring customer objections, and the emotional tone of the conversation. Based on this analysis, the system can provide real-time feedback to sales representatives, suggesting how to respond to objections more effectively or recommending adjustments to the tone of the conversation. Salesforce, for example, uses NLP in its Einstein Voice product to enable voice commands in CRM systems, automating data entry and interaction analysis.

Finally, NLP underpins the automation of routine tasks. Systems can automatically sort incoming emails by urgency or topic, generate concise summaries of long email threads, and even draft personalized response templates, significantly reducing the administrative burden on sales teams.

Implementing NLP in sales processes unlocks significant opportunities. The primary benefit is increased productivity and efficiency. Automating tasks such as CRM data entry, email sorting, and lead qualification frees up sales managers’ time, allowing them to focus on building customer relationships and closing deals. Secondly, NLP enables data-driven decision-making. Analyzing vast amounts of textual information helps uncover hidden trends, understand customer sentiment, and identify the most effective sales strategies. This leads to more accurate forecasts and optimization of the sales funnel. Thirdly, customer service quality improves. Through sentiment analysis and rapid response to inquiries, companies can increase customer satisfaction and loyalty. NLP-powered chatbots provide 24/7 support by instantly answering standard questions.

Despite its clear advantages, the use of NLP comes with certain challenges. One of the main issues is the ambiguity of human language. Sarcasm, irony, cultural nuances, and contextual differences may be misinterpreted by algorithms, leading to incorrect conclusions. Model accuracy strongly depends on the quality and volume of training data; biased data can result in biased models. Data privacy and security are also major concerns, as analyzing customer communications requires processing sensitive information. Finally, there are technical limitations related to supporting rare languages and dialects, as well as the complexity of integrating NLP solutions into existing IT infrastructures.

From a technical perspective, NLP solutions for sales are built on a variety of algorithms and models. For text classification tasks (such as identifying the topic of an email), algorithms such as Naive Bayes classifiers, support vector machines (SVM), and increasingly neural networks—including convolutional (CNN) and recurrent (RNN) networks—are commonly used. Sentiment analysis is performed using both lexical approaches (based on dictionaries with sentiment scores for words) and machine learning models trained on labeled datasets.

Modern systems are dominated by transformer architectures such as BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer). These large language models (LLMs) are capable of capturing complex contextual dependencies in text, making them extremely effective for tasks such as question answering, text generation, and intent recognition. For example, a model can be fine-tuned on a corpus of sales-related texts to accurately recognize industry-specific terminology and purchasing signals.

Predictive Analytics and Machine Learning (ML)

Predictive analytics, based on machine learning (ML) technologies, is a powerful tool for forecasting future events through the analysis of historical and current data. In the sales domain, this approach enables a shift from intuitive judgments and manual calculations to precise, data-driven forecasts. ML algorithms are capable of processing vast amounts of information from multiple sources, including CRM systems, customer behavior data, and market trends, in order to uncover hidden patterns and accurately predict future sales volumes, customer behavior, and the likelihood of deal closure.

The principle behind predictive analytics in sales lies in building mathematical models that are “trained” on historical data. The process begins with data collection and preparation, which may include customer demographic information, purchase history, sales cycle length, seasonal fluctuations, marketing campaign data, and even external factors such as economic indicators. At this stage, data is cleaned of errors and standardized into a unified format.

Next, an appropriate ML algorithm is selected to build the model. The model is trained on a portion of the historical data (the training set), where it identifies correlations and relationships between various variables and the target outcome (for example, sales volume). After training, the model is tested on another portion of the data (the test set) to evaluate its accuracy. If the accuracy is satisfactory, the model is ready to be used for forecasting future outcomes based on new, real-time incoming data.

An important characteristic of ML models is their ability to improve over time: as new data becomes available, they can be retrained, adapting to changing conditions and increasing the accuracy of their predictions.

The primary and most important application of predictive analytics in sales is sales forecasting. Instead of relying on historical averages or subjective managerial estimates, companies can use ML models that account for dozens or even hundreds of factors, including seasonality, marketing campaign effectiveness, competitor activity, and customer behavior. Studies show that this approach can increase forecast accuracy to 73–82%. For example, retail giants such as Amazon and Walmart use AI to forecast demand, enabling them to optimize inventory and logistics. Walmart, through AI-driven analytics, has reduced stock shortages in its warehouses by 30%.

Another application is predictive lead scoring. Models analyze the characteristics of successful past customers and assign new leads a score that reflects their likelihood of conversion. This allows sales teams to focus their efforts on the most promising prospects, increasing overall efficiency. Druva, for instance, used AI-based scoring to evaluate lead quality based on behavioral and demographic data, enabling the company to scale operations without increasing headcount and to shorten sales cycles.

Predictive analytics is also used to identify the risk of customer churn (churn prediction). By analyzing customer behavior—such as reduced purchase frequency, decreased engagement, or negative feedback—the model can accurately predict which customers are likely to leave and trigger proactive retention measures.

The main advantage of using ML for sales forecasting is a significant increase in prediction accuracy. This allows companies to plan resources more effectively, manage inventory, and develop more reliable budgets. Optimizing resource allocation is another key benefit: by focusing efforts on high-potential leads and customers at risk of churn, companies maximize the return on investment in sales and marketing.

Predictive analytics also supports the personalization of customer interactions. By understanding which products or services are most likely to interest a specific customer, companies can make relevant offers, increasing the likelihood of cross-selling and upselling. Ultimately, all of this leads to higher revenue and improved operational efficiency.

Despite its vast potential, the implementation of predictive analytics is associated with a number of challenges. Data quality is a critical factor: incomplete, inaccurate, or outdated data will lead to incorrect models and, as a result, erroneous forecasts. According to statistics, data quality issues affect up to 40% of business initiatives. Another serious challenge is the shortage of qualified specialists—data analysts and ML engineers—capable of building and maintaining such systems. The complexity of integrating predictive analytics with existing CRM and ERP systems can also be a barrier. In addition, there is the “black box” risk, where complex models, such as neural networks, generate predictions but do not easily allow interpretation of which specific factors led to a particular outcome. Finally, ethical considerations related to customer data privacy must be taken into account.

A wide range of ML algorithms is used in predictive analytics for sales. For time-series forecasting tasks (for example, monthly sales volumes), time-series analysis models such as ARIMA (Autoregressive Integrated Moving Average) are widely used, as well as more advanced models based on recurrent neural networks (RNN), particularly LSTM (Long Short-Term Memory), which handle long-term dependencies in data effectively. For regression tasks (predicting a numerical value, such as deal size), linear and polynomial regression are commonly applied. For classification tasks (for example, determining whether a lead will convert) and more complex regression problems, decision trees and ensemble methods such as Random Forest and Gradient Boosting are frequently used. Implementations like XGBoost and LightGBM are especially popular due to their high performance and accuracy. In recent years, artificial neural networks (ANN) and deep learning models capable of capturing complex nonlinear relationships in large datasets have become increasingly widespread.

Generative AI

Generative artificial intelligence is a breakthrough technology capable of creating new, original content—including text, images, code, and other data—based on the analysis of existing patterns. In the sales domain, generative AI is becoming a powerful tool for automating, personalizing, and optimizing customer communications, enabling teams to work more efficiently and achieve better results. It does not merely analyze information but actively participates in value creation at every stage of the sales funnel.

At the core of generative AI are complex deep learning models known as large language models (LLMs), such as GPT-4 or Claude. These models are trained on massive volumes of textual and other data from the internet, books, and various knowledge bases. During training, they learn grammar, syntax, context, style, and factual information. Their operating principle is based on predicting the next most probable word (or data element) in a sequence. When a user provides a “prompt”—a textual request or instruction—the model begins generating a response by sequentially adding words based on probability distributions learned from the training data. In this way, it can write emails, create presentation texts, answer questions, and perform many other tasks, imitating human communication style and creativity. In the sales context, these models can be fine-tuned on a company’s internal data (CRM records, correspondence, documentation) to make their outputs more accurate and aligned with corporate tone and style.

The use of generative AI in sales is extremely diverse. One of the most popular directions is the creation of personalized content for communications. Generative AI–based tools can automatically draft emails to potential customers, adapting the content to their industry, job role, and previous interactions with the company. For example, Microsoft Copilot for Sales integrates with CRM systems and email clients, suggesting email text options, summarizing long email threads, and providing contextual customer information directly while composing a response. This significantly reduces preparation time and increases message relevance.

Another important area is the automation of lead generation and initial outreach. Systems such as Artisan AI use AI agents to automatically identify potential customers, write and send personalized outreach emails, and handle follow-up reminders. This makes it possible to reach a much larger audience without increasing the workload on sales managers.

Generative AI is also used for sales preparation and training. It can create negotiation simulations in which sales representatives practice responding to difficult objections, or generate concise briefs about a client and their company before a meeting by aggregating information from open sources and internal systems. Salesforce Einstein uses generative AI to analyze call recordings and provide real-time recommendations to sales representatives, as well as to automatically generate reports and deal forecasts.

The adoption of generative AI brings numerous benefits to sales teams. First and foremost, it delivers a radical increase in efficiency. By automating routine tasks such as email writing, data entry, and report preparation, AI can free up to 50% of sales representatives’ time, allowing them to focus directly on customer interactions and closing deals. This leads to an increase in both the quantity and quality of engagements. The second key benefit is hyper-personalization at scale. Generative AI enables the creation of unique content for each customer—something that was previously possible only with a very limited number of contacts. This increases engagement and response rates and, according to research, can lead to a 30% or greater increase in win rates. Finally, generative AI improves decision-making quality by providing accurate sales forecasts, analyzing deal health, and identifying potential risks based on communication analysis.

Despite its impressive capabilities, the use of generative AI is not without risks. The primary concern is data quality and confidentiality. Models are trained on data, and if that data is inaccurate, biased, or outdated, the generated content will also contain errors. Using internal company data to fine-tune models requires strict security measures to prevent leaks of sensitive information. Another risk is AI “hallucinations,” where the model generates factually incorrect or fabricated information while presenting it as accurate. This can cause serious reputational damage to a company. There are also challenges related to integration and adoption: effective use of generative AI requires tight integration with existing CRM and other systems, as well as training employees to work with new tools. Finally, excessive automation may lead to a loss of the “human touch” in customer communication, which is especially critical in B2B sales built on trust-based relationships.

The technical foundation of generative AI in sales is typically based on transformer neural network architectures. Models such as GPT (Generative Pre-trained Transformer), BERT, and their derivatives are considered de facto standards. These models consist of multiple layers of encoders and decoders with attention mechanisms that allow them to weigh the importance of different words in context when generating sequences. Deployment in sales environments often occurs via APIs (Application Programming Interfaces) provided by companies such as OpenAI, Google, or Anthropic, which enable LLM functionality to be embedded into proprietary applications and workflows. An important technical aspect is fine-tuning and RAG (Retrieval-Augmented Generation). Fine-tuning is the process of further training a pre-trained model on company-specific data (for example, a knowledge base or communication history) to improve relevance. RAG is an approach in which the model accesses an external knowledge base before generating a response to retrieve up-to-date information, reducing the risk of hallucinations and enabling the use of the most current data.

Computer Vision (CV)

Computer vision is a field of artificial intelligence that enables computers and systems to “see” and interpret visual information from the surrounding world, such as images and video. In retail sales, this technology is becoming a powerful tool for optimizing operations, improving the customer experience, and enhancing security. By analyzing data from surveillance cameras and other sensors in real time, retailers gain unprecedented opportunities to make decisions based on actual customer behavior and the condition of the sales floor.

Computer vision technologies work by converting visual data into numerical form that machine learning algorithms can process. The process typically includes several stages. First, images are captured using cameras. These images then undergo preprocessing to improve quality (e.g., contrast enhancement, noise reduction). A key stage is feature extraction, during which algorithms identify important elements in the image such as edges, shapes, colors, and textures. Finally, to solve specific tasks—such as object classification (determining whether the image contains a person, a product, or a shelf), object detection (locating objects within the image), and segmentation (identifying the precise boundaries of each object)—deep learning models are used, primarily convolutional neural networks (CNNs). Trained on millions of images, these networks can recognize complex visual patterns with high accuracy.

Specific Applications in Sales

In retail, computer vision has numerous practical applications that directly impact sales. One of the most significant is inventory management and shelf monitoring. Computer vision systems can continuously scan shelves, automatically detecting out-of-stock items, incorrect product placement, or inaccurate price labels. This allows staff to replenish inventory and correct errors promptly, preventing lost sales. For example, Walmart uses robots equipped with cameras that patrol the store floor and identify empty shelves with accuracy up to 14 times higher than manual checks.

Another revolutionary application is cashierless stores. A prominent example is Amazon Go’s “Just Walk Out” technology. A system of cameras and sensors tracks which products a customer picks up from the shelf and automatically adds them to a virtual cart. Upon leaving the store, the purchase amount is charged to the customer’s account, completely eliminating checkout lines and making the shopping process fast and convenient.

Customer behavior analysis is another important area. Computer vision enables the creation of in-store heat maps that show which areas are most visited, where customers spend the most time, and which products they interact with most frequently. This information is invaluable for optimizing store layout, product placement, and marketing campaigns. Retailers such as H&M and Tesco use traffic analysis to improve merchandising, which can increase sales by 10–15%.

Finally, the technology is used for loss prevention. Systems can detect suspicious behavior in real time—such as attempts to conceal items or actions characteristic of theft—and send alerts to security personnel, significantly reducing losses due to shoplifting.

Implementing computer vision in retail sales delivers tangible benefits. Increased operational efficiency is achieved through automation of manual tasks such as inventory checks and monitoring, reducing labor costs and errors. Improving the customer experience is a key factor: eliminating queues, ensuring consistent product availability, and optimizing in-store navigation increase customer satisfaction and loyalty. Sales growth is a direct outcome of these improvements. Preventing out-of-stock situations directly preserves revenue, while customer behavior analytics enable optimization of product placement and promotions for maximum conversion. According to some estimates, retailers can increase sales by 10–20% through better product placement and higher engagement.

Despite its advantages, there are significant challenges. High implementation costs are one of the main barriers. Expenses for high-quality cameras, real-time video processing infrastructure, and software development can be substantial, especially for small and medium-sized businesses. Data privacy concerns are another critical issue. Continuous video surveillance raises ethical questions and requires strict compliance with data protection regulations such as GDPR. Data anonymization is essential to protect individuals’ privacy.

Technical challenges—such as dependence on lighting conditions, potential recognition errors, and the complexity of integrating with existing POS and ERP systems—can also hinder deployment and reduce system reliability.

From a technical standpoint, most modern computer vision systems are built on convolutional neural networks (CNNs). For object detection, popular architectures include YOLO (You Only Look Once), SSD (Single Shot MultiBox Detector), and Faster R-CNN, which can detect and classify multiple objects in real time within a single image. For semantic segmentation tasks—important for precise scene analysis, such as distinguishing customers from shelves—architectures like U-Net and DeepLab are commonly used. Data processing can be performed either on-premises or using cloud-based computing, enabling greater scalability. Hardware is also a crucial component: real-time video processing often requires powerful GPUs or specialized AI accelerators.

Conversational AI

Conversational AI refers to a set of technologies that enable users to interact with computer systems using natural language via text or voice. In sales, these technologies—most commonly implemented as chatbots and virtual assistants—have become indispensable tools for automating communications, instantly qualifying leads, and improving customer service quality. They provide 24/7 availability and a personalized approach, which is critical in modern markets where response speed and interaction relevance determine success.

Conversational AI operates at the intersection of several key technologies. At its core is natural language processing (NLP), which allows the system to understand user requests. This process includes intent recognition—identifying the purpose of the request (e.g., “check pricing,” “schedule a demo”)—and entity extraction, which pulls out important details from the message (e.g., product name, date, time). Once the request is understood, a dialog management component determines the next step in the conversation: asking a clarifying question, retrieving information from a knowledge base, or performing an action. Finally, natural language generation (NLG) produces a human-like response. Modern conversational AI systems increasingly leverage large language models (LLMs), enabling more flexible, context-aware, and natural conversations beyond predefined scripts.

In sales, conversational AI is used across all stages of the funnel. Lead qualification is one of the most effective applications. A website chatbot can engage visitors in conversation, ask qualifying questions (about budget, timeline, needs), and determine whether the visitor is a promising lead. If qualified, the bot can immediately schedule a meeting with a sales representative by checking calendar availability and sending invitations automatically. Platforms like Drift specialize in such solutions, helping companies increase meeting volume and shorten sales cycles.

24/7 support and FAQ handling is another important function. Chatbots can instantly answer common questions about products, pricing, and delivery at any time, freeing sales representatives from routine inquiries. This both improves customer satisfaction through immediate responses and allows sales teams to focus on complex requests and closing deals.

Conversational AI is also used for proactive engagement. A bot can initiate a conversation with a visitor who lingers on a pricing page or views a specific product, offering assistance or a special deal. In e-commerce, chatbots help recover abandoned carts by reminding customers of unfinished purchases and offering support to complete the checkout.

The primary advantage of conversational AI is the acceleration of the sales cycle and an increase in conversion rates. By automating the initial contact and qualification stages, companies reduce response time to zero, which is critical because the likelihood of lead conversion drops sharply within the first minutes after an inquiry. Studies show that the use of chatbots can increase lead conversion rates by up to 40%. Scalability is another major benefit. A single chatbot can conduct thousands of conversations simultaneously, which is impossible for a human. This makes it possible to handle all incoming traffic without increasing headcount. Customer experience is improved through instant responses and 24/7 availability. Finally, conversational AI collects valuable data on customer requests and needs, which can be used to improve products, marketing campaigns, and sales scripts themselves.

Despite its advantages, conversational AI also has limitations. One of the main challenges is its limited ability to understand complex and non-standard queries. If a question goes beyond predefined scenarios or the model’s knowledge, the bot may provide an irrelevant response, leading to customer frustration. Therefore, the ability to seamlessly hand off the conversation to a live agent is critically important. The risk of “hallucinations” in LLM-based models, where the bot invents information, also poses a serious threat. Data privacy is another key concern, as customers may share personal information during conversations. Robust encryption and compliance with regulatory requirements are essential. Finally, there is a risk of depersonalization: if the bot sounds too robotic and lacks empathy, it may alienate customers who value human interaction.

The technical implementation of conversational AI can vary. Simple rule-based chatbots follow rigidly defined dialogue trees. They are easy to build but inflexible. More advanced systems use machine learning models for intent recognition and entity extraction. Commonly used frameworks include Google Dialogflow, Microsoft Bot Framework, and Rasa.

The most advanced solutions are built on large language models (LLMs) such as GPT or LaMDA. They do not require rigid scripts and can conduct far more natural and flexible conversations. Integration with internal systems such as CRM platforms (Salesforce, HubSpot) and knowledge bases is carried out via APIs, enabling the bot to access customer and product information and perform actions such as creating new deals or updating contact details.

Sentiment Analysis

Sentiment analysis, also known as opinion mining, is an AI-based technology used to determine the emotional tone of text, speech, or even visual data. It classifies information as positive, negative, or neutral, and in more advanced implementations can recognize a wide range of emotions such as joy, anger, frustration, or urgency. In the context of sales, sentiment analysis becomes a powerful tool for deeply understanding the “Voice of the Customer,” allowing companies to assess customer sentiment in real time, adapt their strategies, and improve the quality of interactions.

Sentiment analysis is based on natural language processing (NLP) and machine learning (ML) techniques. Several approaches exist. The lexical (rule-based) approach uses predefined dictionaries (lexicons) in which each word is assigned a sentiment score (for example, “excellent” +2, “bad” −1). The system calculates the overall sentiment of a text by summing the scores of individual words. This method is simple but does not account for context or sarcasm.

The machine learning–based approach is more advanced. Models (such as Naive Bayes, support vector machines, or neural networks) are trained on large datasets where texts are already labeled by sentiment. During training, the model learns patterns linking words and phrases to their emotional polarity. A hybrid approach combines both methods to achieve higher accuracy. Modern systems often use deep learning, particularly recurrent and transformer-based neural networks, which can capture complex contextual dependencies and linguistic nuances.

Sentiment analysis is widely applied at all stages of customer interaction. One key use case is real-time analysis of calls and correspondence. During a conversation, the system can analyze not only spoken words but also voice tone, identifying signs of frustration, uncertainty, or enthusiasm. If negative sentiment is detected, the system can provide real-time prompts to the sales representative, suggesting a change in approach, offering a discount, or transferring the call to a more experienced colleague. Platforms such as NICE Enlighten AI and Level AI provide these capabilities for contact centers, helping improve conversation outcomes.

Another important area is feedback analysis. Companies collect vast amounts of feedback through surveys (such as NPS), website reviews, social media, and customer support interactions. Manual analysis of this data is impractical. Sentiment analysis enables automated processing of thousands of reviews, identifying overall trends such as widespread dissatisfaction with a new product feature or delivery issues. Aspect-Based Sentiment Analysis goes even further by determining sentiment toward specific aspects of a product or service (for example, “the battery is great (positive), but the camera is weak (negative)”), providing highly targeted insights for improvement.

Sentiment analysis is also used for brand monitoring across social media and news outlets, enabling companies to respond quickly to negative mentions and prevent reputational crises.

The main benefit of sentiment analysis is the ability to make decisions based on a deep understanding of customer emotions. This enables proactive management of the customer experience. By identifying dissatisfied customers at an early stage, companies can take action to resolve issues and prevent churn. Improving products and services is another key advantage, as feedback analysis helps identify weaknesses and understand which features customers value most.

For sales teams, this means more effective communication. By understanding a customer’s emotional state, a sales representative can better structure the conversation, choose the right arguments, and increase the likelihood of successfully closing a deal. In some cases, the use of AI-based coaching powered by sentiment analysis has led to sales growth of 10–30%.

Despite its usefulness, sentiment analysis faces several challenges. Accuracy can be an issue, especially when analyzing complex texts. Sarcasm, irony, and cultural nuances are difficult for algorithms to interpret. For example, the phrase “Oh, great, another problem” may be incorrectly classified as positive. Context also plays a crucial role: the word “unpredictable” may have a negative connotation for a car but a positive one for a movie plot.

Data bias is another risk. If a model is trained on data where certain demographic groups are more frequently associated with negative feedback, it may reproduce this bias. Finally, analyzing voice tone and facial expressions (in video) raises serious privacy and ethical concerns.

From a technical perspective, various models are used for sentiment analysis. Simple implementations may rely on a bag-of-words approach combined with classifiers such as Naive Bayes or support vector machines (SVM). More advanced approaches use word vector representations (Word2Vec, GloVe), which better capture semantic relationships.

In modern systems, deep learning architectures dominate. Recurrent neural networks (RNNs) and their variants, LSTM and GRU, are well suited for analyzing sequential data such as text. However, transformer-based models such as BERT and RoBERTa currently deliver the best results. They can analyze context bidirectionally, which significantly improves accuracy. For aspect-based sentiment analysis, specialized models are developed that first identify aspects within the text and then determine the sentiment associated with each one.

Sources

Share

Author

Want to know more? Follow us on social media

Subscribe

Company

+373 7 66 12 222

Call us:

Quick Links