Social Media Give Organizations of All Kinds Better Insight into Their Customers
The rapid growth of social media has transformed communication worldwide. People now use online platforms not only to connect with friends and family but also to review products and discuss political issues. There’s also been an explosion in the online expression of personal feelings about just about everything. This massive amount of user-generated content creates an opportunity for organizations of all types to understand how people feel about specific topics.
Social Media Monitoring (aka Social Listening) is a technology that tracks and collects data from different sources online. Monitoring tools scan social networks for mentions of brands, products, competitors, or specific concepts. Companies use these tools to observe public discussions and respond quickly to customer concerns. For instance, if a customer complains about poor customer service on X (formerly Twitter), a company can identify the issue immediately and provide assistance before the problem escalates. This prompt remediation helps businesses maintain a positive reputation and strengthen customer relationships.
Sentiment Analysis Is Key to Social Media Monitoring
An AI technology called Sentiment Analysisis a critical component of Social Media Monitoring. It works by analyzing unstructured text and identifying it as expressing positive, negative, or neutral sentiments. For example, when customers post reviews about a smart phone, Sentiment Analysis can determine whether users are satisfied or dissatisfied with the product.
Early Sentiment Analysis products relied on emotion-carrying terms to assess the overall sentiment of a given text: positive words like “fantastic” or negative ones like “awful” would cause a first-generation sentence- or document-level Sentiment Analysis system to classify a sentence or post as positive or negative. Some systems would add a multiplier factor if, for example, a word like “really” occurred before “fantastic” or “awful.” In a crude way, Sentiment Analysis could begin to quantify the strength of the sentiment.
Users of Sentiment Analysis soon realized that this approach was not sufficient. It did not connect the sentiment to the actual entity that the sentiment was about. It could only categorize the entire post (or any text). Consider the following post:
“I like my new smart phone a lot, but I really don’t like the high price.”
There was no way that document-level Sentiment Analysis could distinguish the two opposing sentiments about different aspects of the product in the post. It could not identify one sentiment for the smart phone as a whole (“like … a lot”) and a second for the smart phone price (“really don’t like”). Clearly, however, businesses would like to get down to this level of granularity to really get a handle on how the customer population is feeling about their products or services. With that level of detail, a company can make decisions on what to improve.
Entity-Based Sentiment Analysis
Understanding a post like the one about the smart phone requires more sophisticated Natural Language Processing (NLP) capabilities called Entity-Based Sentiment Analysis. First of all, Entity-Based Sentiment Analysis recognizes any types of entities mentioned that may be the object of a sentiment, such as people, companies, products, and brands, and understands what kind of entity it is. For example, Apple is a company, iPhone is a product.
Next, the sentiments expressed about the entities are recognized through a NLP-based text analysis of the context. Examples of explicit positive sentiment around entities include:
“I totally recommend XYZ Corporation.”
“Go to Joe’s Pizza. It’s improved a lot.”
In the first example, for instance, Sentiment Analysis knows that “XYZ Corporation” is the object of “recommend,” and thus the sentiment expressed is about that entity. Furthermore, when a sentiment is recognized in this way for an entity, Entity-Based Sentiment Analysis would also assign the sentiment to a specific sentiment type. In the two cases above, the sentiment type could be RECOMMEND.
This last step is critical to aggregating information over large numbers of sentiments because natural language has many, many ways of expressing the same sentiment (instead of “I totally recommend,” it could be “I would endorse” or an indefinite number of other expressions). Assigning a sentiment to a sentiment type solves this problem by allowing the classification and aggregation of similar sentiments that are expressed differently.
Aspect-Based Sentiment Analysis
There’s an even more sophisticated level of Sentiment Analysis, and that is Aspect-Based Sentiment Analysis. Here the sentiment is not just about an entity or a product but provides a finer-grained analysis of different aspects of entities. To give an example: in a restaurant review, the aspects of a restaurant that someone dining there might comment on would include quality of service, quality of food, price, noise level, etc. For instance, here’s a typical restaurant review:
“I enjoyed dining at the Carlisle Restaurant. The staff was unfailingly helpful and friendly. The service was fast. Prices were moderate. Food was excellent. The only thing I didn’t like was that the noise level in the place was too loud.”
SENTIMENT TYPE: LIKE
PREDICATE: enjoyed
OBJECT: Carlisle Restaurant
ASPECT: dining
SENTIMENT TYPE: LIKE
PREDICATE: helpful
PREDICATE: friendly
OBJECT: Carlisle Restaurant
ASPECT: staff
SENTIMENT TYPE: LIKE
PREDICATE: fast
OBJECT: Carlisle Restaurant
ASPECT: service
SENTIMENT TYPE: LIKE
PREDICATE: moderate
OBJECT: Carlisle Restaurant
ASPECT: price
SENTIMENT TYPE: LIKE
PREDICATE: excellent
OBJECT: Carlisle Restaurant
ASPECT: food
SENTIMENT TYPE: DISLIKE
PREDICATE: loud
OBJECT: Carlisle Restaurant
ASPECT: noise level
Advanced Sentiment Analysis Normalizes the Extracted Data
As mentioned above, using more Advanced Sentiment Analysis, all statements expressing the same sentiment (e.g., “I hate X” vs. “I detest X”) will receive the same sentiment type in the output. In addition, the sentiment objects and aspects are normalized (e.g., in the above example the aspect “prices” is normalized to “price” and the predicate “too loud” is normalized to “loud”). This allows the sentiment data to be aggregated under specific normalized object or predicate terms over very large collections of content so that it can be analyzed at a more granular level.
A popular visual display of this aggregated data is in the form of a dashboard where the sentiment data can be sliced and diced, for instance, by its positive and negative aspects. If needed, the user can drill down to the source text in order to get confirmation of a sentiment’s accuracy. In addition, by using location metadata and automated geocoding of mentioned place entities, sentiment can be plotted on a map to predict, for instance, where there will be higher demand for which products.
Sentiment Analysis and Social Media Monitoring Are Critical for Business Success
One of the major contributions of Sentiment Analysis and Social Media Monitoring is their role in business decision-making. Companies can analyze customer feedback to improve products and services. By understanding consumer preferences, businesses can create targeted marketing campaigns that better meet customer expectations. For example, if many users express positive opinions about a product feature, companies may focus on promoting that feature in advertisements. Similarly, negative feedback can reveal weaknesses that require improvement. This customer-centered approach increases satisfaction and loyalty.
Businesses can also use Sentiment Analysis and Social Media Monitoring to gain intelligence on their competitors. By collecting information on how their competitors’ products are doing—how customers respond to their existing product features, overall product quality, or pricing, etc.—businesses can get a very fine-grained understanding of the competitive environment they are in.
Another important application of these technologies is brand reputation management. In today’s interconnected world, a single negative post can spread rapidly and damage a company’s image. Social Media Monitoring powered by Sentiment Analysis allows organizations to detect harmful discussions early and respond effectively. During crises, businesses can use Sentiment Analysis to measure public reaction and adjust communication strategies accordingly. For example, airlines often monitor passenger complaints on social media to address delays or service disruptions quickly. Timely responses help reduce customer frustration and protect brand credibility.
Governments and political organizations also use Sentiment Analysis and Social Media Monitoring to understand public opinion. During elections, political parties analyze online discussions to evaluate voter attitudes toward candidates and policies. Governments may monitor social media to identify public concerns or measure reactions to new laws and policies. These insights help policymakers make informed decisions and improve communication with citizens.
In addition to government, politics, and business, Sentiment Analysis also has applications in:
- Healthcare: Researchers can analyze social media posts to, for example, monitor discussions of symptoms and illness to track possible disease outbreaks.
- Education: Educational institutions can use Sentiment Analysis to understand student feedback and improve learning experiences.
- Business: Financial analysts can monitor social media discussions about companies and stock markets to predict investment trends.
These diverse applications demonstrate the broad impact of Sentiment Analysis across multiple industries.
Summary
With a technology like Sentiment Analysis, organizations can get a clear and granular idea of what very large numbers of people are saying on social media about their products or services as well as other aspects of their business. Sentiment Analysis helps companies in many areas such as customer service, brand reputation management, brand promotion, market research, and competitive intelligence.
