Why You Need Sentiment Analysis In Your Company

Img Source - Me-Mind

According to HubSpot, 66% of customers expect companies to understand their needs. However, not all businesses manage to define customer preferences. As a result, 49% of consumers have left a brand in the past year due to poor customer experience.

But how can companies tap into the minds of demanding consumers? Enter sentiment analysis, a technology that can mine opinions, understands your customers, and monitor brand image. Today, we’ll have a look at this trailblazing technology and explore the most popular sentiment analysis examples.

What is sentiment analysis?

Sentiment analysis is a field of natural language processing that discerns opinions and emotions in text data. Be it a tweet, review, or email, sentiment analysis extracts subjective information in source material and classifies the emotion behind this input. As a result, businesses can automatically monitor social sentiment and adjust their offerings, products, and services accordingly.

While this technology sounds far-fetched and sci-fi, it has already taken off in many areas. Below, we’ll go over some of the real-world sentiment analysis examples.

You can find more applications of sentiment analysis in business here: https://theappsolutions.com/blog/development/sentiment-analysis-for-business/

Social media listening

Social media monitoring is probably the most cited application of sentiment analysis. In 2022, 97% of Fortune 500 companies rely on social media. Therefore, this type of analysis is increasing in value every day.

As such, social media listening tools allow brands to sift through social platforms and analyze comments that users leave about the company. Moreover, some of these tools are used to learn more about your competition and its online positioning.

Using the insights acquired through social media monitoring, brands also can:

  • Create granular content to address the exact needs of their customers
  • Introduce more targeted social media campaigns
  • Make their social media strategy more data-laden
  • Offer personalized customer service and offers
  • Create or adjust existing products or services based on demand

Uber, for example, implemented social media monitoring and text analytics tools to discover if users like the new app version. According to their Marketing Lead, the company makes great use of this technology that helps them understand whether the changes are greeted with enthusiasm. 

Reputation management

Online reputation management (ORM) is another application field of emotion analysis. The technique of influencing information to shape the public’s view of a firm or organization is known as reputation management. Reputation management is a significant concern for today’s businesses, from negative tweets to timely responses.

ORM tools rely on sentiment analysis to enable firms to:

  • Access mention insights
  • Grow brand’s reputation
  • Amend negative brand awareness
  • Find relevant influencers, etc.

Customer support ticket analysis

Customer support management has always been a dread for companies. Troves of requests, topics, and branches make customer service even more challenging. In the good old days, all these requests used to be processed manually. Today, automated customer service centers ease the strain,

Sentiment analysis, among others, helps brands boost customer satisfaction and provide quick TATs for urgent issues. Thus, intelligent software dives into the request for meaning, emotion, and tone. If the issue is urgent, the tool makes it prioritized to solve immediately.

Prioritized queries can be routed to human agents, while simple queries are addressed by a bot or automated reply. Furthermore, smart software can assign sentiment scores to each reply, thus assessing the level of customer support.  When combined with other customer experience indicators, sentiment analysis results can provide a more comprehensive picture of what consumers think and feel.

Market research and competitor analysis

Market research is perhaps the most common application of sentiment analysis, aside from brand perception and consumer opinion investigation.

It’s worth noting that sentiment analysis isn’t the only instrument available for market research.

However, it can provide a unique viewpoint on the industry and provide some useful insights into how the situation is perceived by consumers on the ground. You can also use a similar strategy to evaluate the competition and their marketing activities. Altogether, these applications can help companies adjust their value proposition based on market trends and competitors.

Thus, prior to the 2012 presidential election, the Obama administration employed sentiment analysis to evaluate public reaction to policy announcements and campaign statements (more info). It’s easier to strategize and plan for the future when you can easily see the sentiment behind everything from forum postings to media outlets.

Sentiment analysis: How it works

Now that you’re aware of the most popular applications, let’s look under the hood of sentiment analysis. The science behind this concept comes down to machine learning and NLP. The latter classifies text data as positive, neutral, or negative. However, the mileage can vary.

Automatic algorithms

This type of algorithm relies on machine learning techniques and their ability to analyze troves of data. The algorithm is first trained on a dataset that includes a large number of texts classified either as positive, negative, or neutral. After that, as the algorithm learns, it requires less manual input. Although the rationale behind the algorithm’s classification stays unclear, it can successfully tackle loads of text data.


This type of algorithm is powered with manually created lexicons. These classify the strings of words as positive or negative. Based on the number of each, the algorithm then produces a score. This method is one of the easiest to employ since it is transparent and allows for exploration of the rules behind the algorithm. 


As the name implies, this algorithm takes the best of the two. Overall, it is considered to be the most effective one since it combines the automation and transparency of the two algorithms mentioned above. 

The choice of the algorithm depends on the available data and expertise. 

The Final Word

Like all AI branches, sentiment analysis has gained a strong foothold in the market. Powered by NLP, this technology drills into the intent and meaning of text and audio data, which allows companies to extract valuable insights when they need it most. Sentiment analysis is also the pillar of digital must-haves. These include reputation management, social listening, and automated customer support. If you’re looking to build a custom algorithm, make sure you connect with the experts to produce an accurate algorithm.

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