NLP Sentiment Analysis: How It Works, Types, and Challenges in Real Conversations

NLP sentiment analysis is the use of natural language processing to identify whether language expresses a positive, negative, or neutral opinion, and often what that opinion is about. Businesses use it to understand how customers feel in reviews, surveys, chats, emails, and phone calls without reading or listening to each one.

Sentiment analysis sounds simple: decide whether something is good or bad. In practice, customers use sarcasm, change their minds mid-sentence, and express frustration about one thing while praising another. Spoken conversations add another layer of complexity, because the model has to work from a transcript of unscripted speech rather than polished written text.

This guide explains how NLP sentiment analysis works, the main approaches and types, why real conversations are harder to analyze, and what to look for in a sentiment analysis tool for customer calls.

What is NLP sentiment analysis?

Sentiment analysis, sometimes called opinion mining, is a natural language processing task that classifies the emotional tone of language. The output is usually a label (positive, negative, or neutral), a score on a scale, or both.

NLP is the broader field of AI that helps computers understand and generate human language. Sentiment analysis is one application of NLP, alongside tasks like transcription, summarization, topic detection, and intent recognition. In the contact center, sentiment analysis is one of several NLP use cases in customer service, along with AI agents, real-time agent guidance, and AI-predicted CSAT.

Sentiment analysis vs. emotion detection vs. intent

These three tasks are related, but they answer different questions:

A customer who says "I've called three times about this refund" has negative sentiment, is likely frustrated, and intends to get a refund. Customer service teams often use these three signals together.

How does NLP sentiment analysis work?

NLP sentiment analysis generally follows five steps:

1. Capture and transcribe: Text from reviews, chats, and emails is ready to analyze. For phone calls, automatic speech recognition (ASR) first converts speech into a transcript.

2. Preprocess the text: The system splits text into sentences and words, normalizes spelling and casing, and, for transcripts, restores punctuation and handles filler words.

3. Represent the text: The model converts words into a format it can process, such as word counts, embeddings, or the contextual representations used by transformer models.

4. Classify and score: The model assigns a sentiment label or score to each unit of text, whether that's a whole document, a sentence, or a specific entity.

5. Aggregate and deliver: Scores roll up into something people can use, such as a sentiment trend on a live call, a dashboard across thousands of conversations, or an alert for a supervisor.

Approaches to sentiment analysis in NLP

Lexicon and rule-based approaches

Lexicon-based systems use dictionaries of words tagged as positive or negative, plus rules for handling negation ("not happy") and intensifiers ("very happy"). They're transparent and fast, but they struggle with context. "This product is sick" can be praise or criticism, and a word list can't tell the difference.

Machine learning approaches

Traditional machine learning models learn sentiment patterns from labeled examples instead of fixed word lists. They handle context better than lexicons, but their accuracy depends heavily on how closely the training data matches the language they'll analyze in production.

Transformer models

Transformer models such as BERT and its smaller variants read each word in the context of the words around it, which helps them handle negation, sarcasm cues, and phrasing that depends on context. Dialpad researchers used this kind of model in their work on entity-level sentiment analysis in contact center telephone conversations, presented at EMNLP 2022. The paper compares two approaches: one built entirely on the transformer-based DistilBERT model, and another that pairs a neural network with heuristic rules.

Large language models

Large language models (LLMs) like ChatGPT can perform sentiment analysis when given clear instructions, and they can explain their reasoning in plain language. They're useful for flexible, one-off analysis, but real-time customer service has additional constraints: cost, latency, and consistency across millions of conversations.

A related lesson comes from Dialpad research on a different real-time task. In Topic Matching in the Wild, Dialpad researchers found that lightweight LLMs given plain-language topic descriptions outperformed both regex matching and sentence embeddings on real contact center transcripts, and larger models didn't improve results. The study focused on topic recognition rather than sentiment, but the principle carries over: the right model for a live conversation is the one that performs well under real conditions, not necessarily the largest one.

Types of sentiment analysis

The three main types of sentiment analysis differ by how much text they score at once.

• Document-level: Assigns one sentiment to an entire piece of text, such as a product review or a full call transcript. It's useful for quick summaries but can hide mixed feelings.

• Sentence-level: Scores each sentence separately. This shows how sentiment shifts over the course of a conversation, such as a call that starts frustrated and ends satisfied.

• Entity-level or aspect-based: Identifies sentiment toward specific things mentioned in the text, such as a product, a feature, a policy, or a company. A customer might love the product and dislike the billing process, and entity-level analysis captures both.

Entity-level analysis is often especially useful for businesses because it points to what's driving customer feelings. Dialpad's entity-level sentiment research, described in the transformer models section above, was designed for exactly this purpose: analyzing English telephone conversation transcripts in contact centers to understand how customers feel about specific products or companies they mention.

Why sentiment analysis is harder in spoken conversations

Many sentiment models are trained and tested on written text like product reviews and social media posts. Customer calls are different in several ways.

Spontaneous speech is messy

In the Topic Matching in the Wild study, the human-annotated evaluation dataset drew from customer service calls across 11 companies and 173 topics. Nearly a quarter of utterances (24.2%) contained filler words or discourse markers, and 11.4% contained immediate word repetitions. Dialpad's research team explains why these conditions matter in When "money back" means "refund", an overview of what the study found.

Transcripts depend on speech recognition

Each transcription error, crosstalk moment, or noisy line passes downstream to the sentiment model. A misheard word can flip the meaning of a sentence.

Sentence boundaries aren't obvious

People don't always speak in punctuated sentences, but sentence-level sentiment depends on knowing where one thought ends and the next begins. Dialpad researchers have published work on improving punctuation restoration for speech transcripts, which helps make transcripts readable and easier to analyze.

Tone carries meaning that words don't

"Great, thanks" can be sincere or sarcastic depending on how it's said. Sarcasm is a known weak point for text-based sentiment models, which often label sarcastic remarks as positive because they contain positive words. Researchers are also exploring signals beyond the transcript, including a study co-authored by a Dialpad researcher (Unsupervised Emotional Pattern Recognition Using Rhythmic and Vocal Features) on recognizing emotional patterns from rhythmic and vocal features in speech.

Why explainability matters in sentiment analysis

A sentiment score is only useful if people trust it. When a supervisor sees a call flagged as negative, they need to understand why before they act on it.

Explainability techniques show which words or phrases most influenced a model's decision. Dialpad's AI team described how it approached this in making a sentiment model explainable. Users of an earlier version of the sentiment feature didn't always understand why certain sentences were tagged positive or negative, especially long sentences or ones with subtle sentiment. The team chose to highlight the words that most influenced each prediction, a format that user studies indicated was helpful.

Explainability also exposes judgment calls. Should a swear word count as negative in a business conversation? Some teams would say yes, while others would judge it by context. Making the model's reasoning visible helps teams catch errors, spot subjective edge cases, and decide how much weight to give a score.

How contact centers use sentiment analysis

In customer service, customer sentiment analysis is especially valuable when it happens in real time and connects to action.

• Live call monitoring: With real-time sentiment analysis, supervisors can see how customer conversations are trending across their team. If a call turns negative, they can open the live transcript for context and decide whether to step in or barge into the call to help de-escalate.

• AI agent handoffs: When Dialpad AI Agents escalate a conversation to a human, customer sentiment is part of the context passed to the agent, along with the reason for the call and the troubleshooting steps already attempted.

• Coaching and QA: Sentiment patterns across calls help managers identify coaching opportunities and review the conversations that need attention first.

• Measuring satisfaction: Sentiment and customer satisfaction are related but different. Sentiment describes tone in the moment, while CSAT reflects how satisfied a customer is with the overall interaction. Dialpad AI CSAT predicts satisfaction from call transcripts, building on Dialpad research into predicting customer satisfaction, so teams aren't limited to the small share of customers who complete surveys.

What to look for in a call sentiment analysis tool

If you're evaluating sentiment analysis for customer calls, these criteria can help separate tools built for real conversations from general-purpose sentiment analysis tools designed mainly for written text like social media posts and reviews.

• Transcription accuracy on real calls: Sentiment analysis on calls is only as good as the transcript. Ask how the tool performs with background noise, crosstalk, accents, and industry-specific vocabulary.

• Real-time and post-call analysis: Real-time sentiment lets supervisors act during the conversation. Post-call analysis supports coaching, QA, and trend reporting. Many teams need both.

• Entity-level or aspect-based insight: A single positive or negative score tells you how a customer felt, not why. Look for tools that connect sentiment to specific products, policies, or issues.

• Explainable results: Supervisors should be able to see what drove a sentiment score, so they can trust it and catch errors.

• Evaluation on conversation data: Ask whether the model was tested on real customer conversations rather than only on written text like reviews.

• Connection to workflows: Sentiment is especially useful when it triggers action, such as supervisor alerts, coaching workflows, QA reviews, or context for AI agent handoffs.

How accurate is sentiment analysis?

Accuracy varies widely depending on the model, the data it was trained on, the type of sentiment analysis, and how closely the production language matches the training data. A model that performs well on product reviews may struggle with customer calls, and even human reviewers can disagree about the sentiment of an ambiguous sentence.

A reliable way to judge accuracy is to test a tool on your own conversations. Compare its results against human review on a sample of calls, look closely at edge cases like sarcasm and mixed sentiment, and check whether the tool explains its scores well enough for your team to catch mistakes.

Turn customer sentiment into operational insight

Sentiment analysis tells you how customers feel. The value comes from connecting that signal to the rest of the conversation, including what the customer wanted, what happened next, and whether the issue was resolved. With conversation intelligence built into the same platform your teams use for calls, messaging, and contact center work, Dialpad connects sentiment to the full context of each customer conversation, so teams can make better-informed decisions.