Written by Identrics Contents on page
Ever tried to figure out what people really think about your product, only to feel like you are reading hieroglyphics? You are not alone. In our hyper-connected world, we are swimming—or sometimes drowning—in a sea of text data. Tweets, reviews, blogs, comments—you name it. It is like trying to drink from a firehose of information.
General sentiment analysis has been our trusty compass, pointing us in the general direction of public opinion. But let’s be honest—it often feels like using a magnifying glass to find a needle in a haystack. Sure, you might know that people are generally happy or upset, but what exactly is making them feel that way? That is the million-dollar question.
This is where entity-based sentiment analysis comes ahead of its predecessor, general sentiment analysis. Instead of just telling you the overall mood, it zeroes in on the specifics—like a detective with a magnifying glass focused on the clues that matter most.
Rather than knowing that people are generally satisfied or dissatisfied, you will discover exactly what they are talking about. You will be able to find answers to questions such as:
- Is there a surge in misinformation about a particular event affecting public perception?
- Or perhaps a spike in negative sentiment toward a company that could influence its stock price?
What is advanced entity-based sentiment analysis?
So, what on earth is entity-based sentiment analysis? Think of it as the Sherlock Holmes of text analytics—it does not just tell you what people are feeling but who or what they are feeling it about.
In simple terms: It is the process of detecting and interpreting sentiments expressed toward specific entities or aspects within a text.
Imagine you are scanning through a news article about your company that says:
“While XYZ Corp’s latest smartphone boasts excellent features and sleek design, its customer support has been underwhelming, leaving many users frustrated.”
The result from a general sentiment analysis will be neutral sentiment overall. However, if we use entity-based (targeted) sentiment analysis, it will break down the sentiments associated with specific entities.
In the example above, you will get:
| Entity-based sentiment analysis insight | |
| Positive sentiment towards | Negative sentiment towards |
| XYZ Corp’s latest smartphone | Customer support |
By using entity-based sentiment analysis, you can discover that while your new product is a hit, your customer support is dropping the ball. This granular insight of targeted sentiment analysis is invaluable for media intelligence, for example, as it allows your PR and customer service teams to address specific issues rather than making broad, unfocused efforts.
How does it differ from general sentiment analysis?
Now that we have explored what entity-based sentiment analysis is, let’s spotlight how it stands apart from general sentiment analysis.
Mostly, it differs in 2 major ways:
- It has a granular focus
Think of general sentiment analysis as getting a general vibe of a conversation—it is like knowing everyone at the party is either happy or grumpy, but not knowing why Uncle Bob is frowning in the corner.
| General sentiment analysis | Entity-based sentiment analysis | |
| Overview | Provides a general sentiment for the entire text | Associates sentiments with individual entities or aspects |
| Limitation / Advantage | Lacks specificity about which aspects or entities the sentiment is directed toward | Offers detailed insights into specific elements within the text |
In a nutshell, general sentiment analysis tells you what people are feeling, while entity-based sentiment analysis tells you who or what they are feeling it about. It is the difference between knowing your customers are unhappy and knowing they are specifically upset about your app’s latest update crashing more often than a clumsy ice skater.
- It offers enhanced insights
Why does this granular focus matter? Because in the world of business, details are gold.
Understanding exactly what customers are talking about and how they feel about each specific entity allows you to identify precise areas that need improvement adjust your business efforts and focus your efforts where they will have the most impact.
What kind of entities can you monitor using entity-based sentiment analysis?
So, what exactly are these “entities” we have been chatting about? In the realm of text analytics, entities are the key elements within a text that hold significant importance.
They are the nouns that carry weight—the subjects of our sentences and the focus of our opinions.
In a business context, entities can be:
- People: Celebrities, politicians, people you work with.
- Organisations: Companies, non-profits, government bodies.
- Products/services: Smartphones, software, that new streaming service everyone is talking about.
- Locations: Cities, countries.
- Events: Concerts, conferences, the annual office dodgeball tournament.
Why do entities matter?
Entities are not just words on a page; they are the heartbeat of our conversations.
When we express opinions or emotions, we are usually directing them toward specific entities. Whether it is a company, a product, a place, or even that barista who always misspells your name, entities are the focal points of our sentiments.
They matter because:
| They provide context | Knowing the entity gives meaning to the sentiment. Saying “I love it” is nice, but knowing what you love—be it a new phone, a customer service experience, or your colleague’s homemade cookies—makes all the difference. |
| They drive action | For businesses, understanding which entities are associated with positive or negative sentiments enables targeted improvements. It is like having a roadmap that points directly to areas needing attention. |
| They enhance understanding | In fields like media intelligence, identifying entities helps organisations grasp public opinion’s nuances. Are people raving about your latest product launch or criticising your recent ad campaign? |
To put it into perspective, imagine scrolling through social media and seeing posts like:
- “The new XYZ laptop is blazing fast, but the battery dies quickly.”
- “Had a fantastic experience with the media intelligence platform of ZYC, but the customer service could be better.”
- “EcoRide’s electric bikes are changing the game!”
In each case, sentiments are directed toward specific entities—products, organisations, or services. By identifying these entities, businesses can dive deeper into what is driving customer opinions.
Harness the power of entity-based sentiment analysis.
How does entity-level sentiment analysis work?
So, we have talked about what entity-based sentiment analysis is and why entities are crucial in communication. Now, let us pull back the curtain and see how this analytical magic happens.
At its core, entity-based sentiment analysis involves two main steps:
- Named Entity Recognition (identifying “who” or “what”): This is where we find and categorise entities within the text. Think of it as highlighting all the key players in a script.
- Entity-Based Sentiment Classification (understanding the “how”): Here, we determine the sentiment expressed toward each identified entity. Is the mention positive, negative, or neutral?
Breaking down the process
| Step | Description |
| Data collection | Gather text data: The process starts with collecting text data from various sources such as social media, reviews, news articles, forums, and more. It is like collecting pieces of a puzzle to get the full picture. |
| Text preprocessing | Cleaning the data: Remove irrelevant information like HTML tags, special characters, or duplicate spaces. |
| Entity recognition | Named Entity Recognition (NER): Use algorithms to identify entities within the text. Classification of entities: Categorise entities into types such as people, organisations, locations, etc. |
| Sentiment analysis | Contextual understanding: Analyse complex relations and associations to each entity allowing to grasp the sentiment. Sentiment scoring: Assign a sentiment score and a label (positive, negative, neutral) to each entity. |
| Aggregation and reporting | Data compilation: Compile the sentiment scores for each entity across all data sources. Visualisation: Present the findings through charts, graphs, or dashboards for easier interpretation. Insight generation: Extract actionable insights to inform decision-making. You can even receive automated reports or alerts with specific entity sentiment analysis insights. |
Why do we need entity-based sentiment analysis
Entity-based sentiment analysis is not just a buzzword in the tech world; it is a powerful tool that meets specific business needs and offers significant advantages over general sentiment analysis.
Let’s explore why it is essential across various industries and the benefits it brings.
✔ For media monitoring and intelligence
Entity-based sentiment analysis plays a crucial role in helping media organisations and intelligence teams sift through vast amounts of content to gain actionable insights.
It allows these teams to:
- Track public sentiment toward key events, organisations, or individuals in the media.
- Identify problematic narratives or harmful content, such as the rise of false claims about a political event or crisis.
For instance, imagine discovering that a specific social media campaign is generating overwhelmingly positive sentiment about a public health policy. At first glance, this general sentiment might make you believe the campaign is successful.
However, with entity-based sentiment analysis, you could uncover that while the policy itself is receiving praise, critical aspects—like its implementation or impact on specific communities—are drawing negative sentiment.
See our entity-based sentiment analysis in action.
✔ For combatting misinformation and hate speech
Where misinformation and hate speech are observed, entity-based sentiment analysis provides invaluable insights into negative narratives targeting individuals, groups, or organisations. It allows organisations to:
- Identify sources of misinformation
- Monitor hate speech by tracking mentions of specific communities or topics and the sentiment attached to them.
For example, if a spike in negative sentiment is detected toward a marginalised group due to an online misinformation campaign, this analysis helps organisations and policymakers respond proactively and effectively.
✔ For risk and compliance
For risk management and regulatory compliance purposes, entity-based sentiment analysis plays a vital role by helping organisations identify potential threats and ensure adherence to legal and ethical standards.
It makes it much easier to:
- Monitor public sentiment
Organisations can gauge the sentiment toward their industry, organisation, or key stakeholders, identifying early warning signs of reputational risk.
- Assess compliance risks
By tracking negative sentiments tied to regulatory issues or governance concerns, government can analyse the environment.
Entity-based sentiment analysis also assists in trend identification and risk forecasting by analysing sentiments tied to emerging regulatory topics or potential crises. This includes:
- Spotting emerging risks by identifying negative sentiment trends tied to specific legislation, industries, or key players.
- Preparing for regulatory changes by analysing how public and media sentiment shifts in response to proposed policies or legal developments.
For example, if an upcoming regulatory change is generating significant negative sentiment in the media, this analysis can help your team prepare an appropriate communication or compliance strategy.
✔ For publishers
In the publishing industry, entity-based sentiment analysis is instrumental in understanding reader feedback. By examining sentiments toward specific authors, articles, or book genres, publishers can gain precise insights that inform content strategies and improve topic engagement, leading to better editorial decisions.
Understanding public sentiment around published works, editorial stances, or controversial topics is another area where this analysis proves invaluable. It enables publishers to address reader concerns effectively, whether it is clarifying misinformation or adjusting editorial focus to better resonate with their audience.
By enhancing the reader experience through responsiveness to specific preferences and concerns, publishers can foster greater trust and loyalty among their audience, while also positioning themselves as credible and audience-focused leaders in the industry.
Overall benefits of entity-based sentiment analysis
Entity-based sentiment analysis offers several overarching benefits. It provides granular insights, allowing organisations to focus on specific aspects and leading to more targeted improvements. This detailed understanding is essential for refining products, services, and strategies.
Improved decision-making is another significant benefit, as the analysis provides precise feedback that informs strategies and policies. Organisations can make data-driven decisions with confidence, knowing they are based on specific, actionable information.
The enhanced customer experience is also a key advantage. By enabling businesses to address individual customer concerns and preferences effectively through personalisation, customer satisfaction and loyalty are likely to increase.
Finally, entity-based sentiment analysis offers a competitive advantage. By using detailed insights to differentiate from competitors and better meet customer needs, organisations can position themselves more favourably in the market.
How to use entity-based sentiment analysis?
Whether you are monitoring media narratives or tackling harmful online content, Identrics’ entity-based sentiment analysis provides the tools and expertise to make informed, data-driven decisions.
Contact us today to learn how we can help you implement entity-based sentiment analysis into your business effectively.
