Welcome to our conversational guide to sentiment analysis, which will help you understand the basics behind the manual and automatic methods for analysing sentiment in unstructured text. Feel free to read the contents of this guide in order or jump straight to the section that sparks your interest. Here's a list of topics covered in this guide:
- Definition of Sentiment
- Types of Sentiments
- Definition of Sentiment Analysis
- Types of Sentiment Analysis
- Methods Used for Sentiment Analysis
- The Effectiveness of Sentiment Analysis
- Applications of Sentiment Analysis
A sentiment represents feelings associated with an individual's thoughts, views, or opinions expressed in their utterance. It can incline toward one of the emotional polarities, either positive or negative, be mixed or neutral.
What Are the Types of Sentiments?
There are four basic types of sentiments:
What’s a Positive Sentiment?
What’s a Negative Sentiment?
What’s a Neutral Sentiment?
What’s a Mixed Sentiment?
A mixed sentiment represents a situation when opposing sentiment polarities are present in an utterance.
What’s Sentiment Analysis?
Sentiment analysis is a manual or automated process focused on assessing the emotional polarity of textual data to determine an overall emotional charge or provide deeper insights into targeted sentiments associated with specific entities present in each utterance.
What Are the Types of Sentiment Analysis?
There are two distinct types of sentiment analysis, which differ in the level of output granularity. While an overall sentiment analysis provides only an aggregated perspective, targeted sentiment analysis delivers insights into the sentiments associated with each entity present in an utterance.
What’s an Overall Sentiment Analysis?
Overall sentiment analysis determines the dominant emotional charge of an utterance as a whole without getting into the details of the constituent parts involved in calculating the overall score. It provides insights into the broad emotional polarity of an opinion, including scores for all types of sentiments.
What’s a Targeted Sentiment Analysis?
Targeted sentiment analysis provides detailed information about the constituent parts of emotional inclinations associated with specific entities in each utterance, such as brand or product or service names. For example, a mixed sentiment can result from positive sentiment toward a brand and negative sentiment toward the product.
What Methods Are Used for Sentiment Analysis?
One of the human superpowers is undeniably emotional intelligence. It allows us to naturally understand the feelings expressed through words and makes it possible to assess sentiment manually. It took a long time to equip machines with similar capabilities, but it's now possible to leverage technology to analyse sentiment automatically.
What’s a Manual Sentiment Analysis?
Manual sentiment analysis is a method for assessing the emotional polarity of unstructured text, relying on the natural human ability to recognise feelings triggered by specific words and phrases and associate them with relevant entities.
What’s an Automatic Sentiment Analysis?
Automatic sentiment analysis relies on rules, machine learning, or some combination of both approaches, which sometimes results in better accuracy due to combining desirable characteristics of relevant methods for assessing the emotional polarity of unstructured text.
What Are Rule-Based Methods for Sentiment Analysis?
Rule-based techniques for analysing sentiment rely on a set of manually crafted rules to identify the polarity of opinions. They leverage various natural language processing methods from computational linguistics, such as stemming, tokenisation, part-of-speech tagging and parsing, often combined with lists of words and expressions called lexicons.
How Do Rule-Based Methods for Sentiment Analysis Work?
Rule-based methods for sentiment analysis convert words in a text into their root form, count them, and multiply the number of occurrences by their weight coming from the lexicon. The next step of the process involves adding partial results, giving either a negative number, around zero, or a positive number, which translates to a negative, neutral, or positive sentiment.
What’s a Lexicon?
A lexicon contains a list of words and phrases annotated with their weights associated with their emotional polarity expressed by numbers usually ranging from -5 (very negative) through 0 (neutral) to 5 (very positive), such as a list of English words like AFINN.
What Are Intelligent Approaches to Sentiment Analysis?
Intelligent approaches to sentiment analysis don’t rely on predefined rules but leverage machine learning algorithms. They model sentiment analysis as a classification problem, similar to automatic customer intent classification.
How Do Intelligent Approaches to Sentiment Analysis Work?
Intelligent approaches to sentiment analysis rely on sentiment classification algorithms, trained with manually classified examples to create a sentiment classifier model that can autonomously process utterances and assign scores to each sentiment category.
How to Train a Sentiment Classifier Model?
Training a sentiment classifier model requires feeding a sentiment classification algorithm with samples of sentiments classified manually. During this process, it learns how to associate specific words and phrases with their corresponding emotional polarities, and any inconsistencies in training data impact its future results.
What’s a Sentiment Classification Algorithm?
A sentiment classification algorithm is a machine learning algorithm that contains step-by-step instructions teaching machines to classify sentiments based on a list of manually classified examples. It generates a sentiment classification model that represents their learnings.
What’s a Sentiment Classifier Model?
A sentiment classifier model results from training a sentiment classification algorithm with a dataset containing lists of manually classified sentiments acting as role models. It possesses intelligence acquired during the training phase.
What’s a Sentiment Score?
A sentiment score is a result of automatic sentiment analysis. It represents the confidence the sentiment classifier model has about correctly identifying an emotional polarity of an utterance on a scale from zero to one.
How Effective Is Sentiment Analysis?
While sentiment analysis is a highly subjective task, both a manual and an automatic method can be effective in specific scenarios. Even if predictions aren’t always accurate or agreed upon, in most cases, they provide a wealth of insights into customer opinions.
How Effective Is Manual Sentiment Analysis?
Although manual sentiment analysis isn't free from limitations, it has several significant advantages over automatic sentiment analysis, making it an effective solution for assessing overall and targeted sentiment in a small number of utterances without strict time constraints.
What Are the Advantages of Manual Sentiment Analysis?
Manual sentiment analysis relies on emotional intelligence and the natural human's ability to understand relationships between words deeply. Both of these traits are difficult to replicate with technology, which results in several significant advantages over automated solutions, including:
- Understanding Relationships Between Words
- Understanding the Sentiment of Objective Language
- Understanding Irony and Sarcasm
- Understanding Context
Why Is Understanding Relationships Between Words One of the Main Advantages of Manual Sentiment Analysis?
Manual sentiment analysis integrates natural human abilities of understanding relationships between words with emotional intelligence. This powerful combination is perfect for recognising different sentiments and associating them with various entities present in each utterance to produce a fine-grained sentiment analysis.
Why Is Understanding the Sentiment of Objective Language One of the Main Advantages of Manual Sentiment Analysis?
Sometimes an utterance has either a negative or positive sentiment even when it doesn’t contain any emotional statements. People have a deep understanding of the meaning behind objective language and can correctly classify sentiment in these scenarios, which can cause automatic sentiment analysis serious problems.
Why Is Understanding Irony and Sarcasm One of the Main Advantages of Manual Sentiment Analysis?
Irony and sarcasm can be misleading because of the presence of words that might sound optimistic but have the opposite meaning. In other words, phrases typically associated with positive feelings represent negative emotions and vice versa. Manual sentiment analysis offers better results than automated solutions in such scenarios.
Why Is Understanding the Context One of the Main Advantages of Manual Sentiment Analysis?
An utterance's emotional polarity can change depending on the context, which is extremely difficult to identify correctly with technology but comes naturally to humans, which lets manual sentiment analysis achieve better results than automated solutions in such cases.
What Are the Disadvantages of Manual Sentiment Analysis?
Although manual sentiment analysis has several significant benefits, its downsides quickly become apparent when the demand for assessing emotional polarity grows beyond what a single person can handle. When it happens, the following problems start to creep in:
Why Is Inconsistency One of the Main Disadvantages of Manual Sentiment Analysis?
Manual sentiment analysis is highly subjective because past experiences and personal beliefs influence it. It often results in inconsistent results, especially when two or more people are involved in the process. Humans tend to agree about 75% of the time when deciding about the sentiment of an utterance.
Why Is Non-Scalability One of the Main Disadvantages of Manual Sentiment Analysis?
It’s hard to imagine manually going through hundreds or thousands of utterances and assessing their sentiments. And scaling the throughput of manual sentiment analysis up and down in response to the actual demand is complex and inefficient at best.
Why Is Time Inefficiency One of the Main Disadvantages of Manual Sentiment Analysis?
Manual sentiment analysis is a laborious process that takes time and effort. While it can analyse a small number of sentiments from time to time, it cannot identify emotional polarities in real-time to reinforce the automated decision-making process and control the flow of conversational experiences across text and voice.
How Effective Is Automatic Sentiment Analysis?
The accuracy of automatic sentiment analysis reaches 70-80%. Although it isn't free from limitations, it has several significant advantages over the manual process, making it an effective solution for assessing overall and targeted sentiment in a large number of utterances in real-time.
What Are the Benefits of Automatic Sentiment Analysis?
Automatic sentiment analysis recognises emotional polarities in text and enables acting upon them in real-time, but time-efficiency isn't the only advantage over the manual process. It also produces consistent results, which are easily scalable in response to the actual demand.
Why Is Consistency One of the Main Benefits of Automatic Sentiment Analysis?
Manual sentiment analysis depends on personal beliefs and past experiences, which often change from one person to another. Automatic sentiment analysis, on the other hand, relies on the same set of rules or sentiment classification models every time, which guarantees consistent results.
Why Is Scalability One of the Main Benefits of Automatic Sentiment Analysis?
Scaling the manual process is challenging and inefficient at best and sometimes isn't even realistic. Automatic sentiment analysis solves this problem by quickly scaling up or down to adapt to changing demand for sentiment analysis seamlessly.
Why Is Time Efficiency One of the Main Benefits of Automatic Sentiment Analysis?
Automated sentiment analysis can examine emotional polarity in many conversations simultaneously, allowing companies to identify problems and act quickly to remedy potential issues as soon as they manifest and improve customer experience.
What Are the Disadvantages of Automatic Sentiment Analysis?
Automatic sentiment analysis relies on technology, which can sometimes be hard to understand. Although it has improved significantly recently, it might not be as precise as the manual process results in complex scenarios. Some of its limitations include:
- Not Understanding the Sentiment of Objective Language
- Difficulty in Uncovering Constituent Parts of Mixed Sentiment
- Not Understanding Irony and Sarcasm
- Difficulty in Understanding Context
Why Is Not Understanding the Sentiment of Objective Language One of the Main Downsides of Automatic Sentiment Analysis?
Automatic sentiment analysis relies on the presence of emotionally charged expressions in the text. When there aren’t any, it classifies the sentiment of the utterance as neutral. However, this approach falls short when the text contains objective language expressing positive or negative sentiment.
Why Is Difficulty in Uncovering Constituent Parts of Mixed Sentiment One of the Main Downsides of Automatic Sentiment Analysis?
Mixed sentiment signalises that there were opposing polarities of opinions present in the analysed text. What makes things even more challenging is the possibility that these polarities relate to different entities, such as a brand and a product or service. The overall sentiment analysis doesn't detect the constituent parts, and it might be necessary to follow up with a targeted sentiment analysis to uncover the details.
Why Is Not Understanding Irony and Sarcasm One of the Main Downsides of Automatic Sentiment Analysis?
While irony and sarcasm aren't usually difficult to identify during a manual process, they are challenging for automatic sentiment analysis due to the presence of exaggerated statements with contrasting meanings. It can result in the wrong classification of the sentiment of an utterance to the opposite category.
Why Is Difficulty in Understanding Context One of the Main Downsides of Automatic Sentiment Analysis?
The emotional polarity of any statement can change based on the context. While it comes naturally to humans, it's challenging for automatic sentiment analysis to consider conversation context when assessing the emotional polarity of an utterance.
What Are the Applications of Sentiment Analysis?
How companies deliver products and services is just as important as what they deliver. Customers contact businesses through various conversational channels, and it’s hard to stay on top of all these interactions. Sentiment analysis helps improve the flow of dialogues and analyse past conversations.
How to Apply Sentiment Analysis to Improve the Flow of Conversations?
Automatic sentiment analysis can detect disgruntled customers instantly by noticing the first signs of discontent. It helps to reduce potential consequences before customer dissatisfaction escalates or spreads across their social circle by changing the flow of conversation and handing it over to a human agent.
How to Apply Sentiment Analysis to Past Conversations?
While analysing the overall sentiment of past conversations across channels and modalities can be a quick and effective way to find out what worked well and what didn't, leveraging targeted sentiment provides finer-grain results by associating consumer opinions and feelings with brand or product or service names.
How to Use Targeted Sentiment to Analyse Brand Perception?
Targeted sentiment enables tracking opinions and emotions consumers associate with your brand and monitoring changes over time. Brand sentiment is one of the best indicators of customer loyalty because it reflects the perceived value of the products or services you offer and the quality of customer service.
How to Use Targeted Sentiment to Analyse the Perceived Value of Products and Services?
Consumer sentiment towards a specific product or service can be an invaluable source of information about customer needs and expectations. It reflects the perceived value of your offerings and provides insights into what parts need improvement. Targeted sentiment can analyse the feelings associated with a particular product or service and monitor how they change.
Analysing the emotional polarity of consumer opinions expressed during natural conversations enables businesses to glean new insights, understand consumers better, and empower specialised teams to be more productive.
Sentiment analysis relies on the ability to understand emotions, but emotional intelligence used to be a superpower reserved for human beings. However, recent technological advancements enabled conversational bots to associate opinions with feelings.
While manual sentiment analysis can be successfully applied to a small number of utterances and has advantages in complex scenarios, it also has numerous limitations that prevent using it at scale or in real-time decision-making.
Although automatic sentiment analysis has limitations, it's worth the effort, even if predictions aren't always accurate, because its advantages save time and money and enable real-time decisions to improve conversation flow, which is an indispensable tool for all companies in the conversational era.