A Guide To Using Sentiment Analysis

A Guide To Using Sentiment Analysis

Dec 17, 2021 12 min read

Social media marketers and managers gauge their social media strategy by checking metrics such as engagement, mentions, and follower growth. However, the quantitative measure of these metrics doesn't reveal how your audience feels about your brand and your services.

Sentiment analysis in social media helps gather information about your clients' or fans' feelings and perceptions. It analyzes a piece of text, potentially a status update or a comment, and categorizes them as either a positive one or a negative one (or neither of these).

In this blog post, we shall explore why sentiment analysis is essential for your brand health and how to implement it for your social media campaigns.

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What Is Sentiment Analysis?

Until a few years ago, analyzing customers' sentiments was not taken seriously by businesses. Today, because of the technological advancements and the changed thinking of businesses, sentiment analysis is emerging as a viable tool.

What makes it exciting and different from the other data analytics form is that it deals with emotions, and emotions never lie (as we all know).

Sentiment analysis tells an enterprise or a brand what the world and consumers feel about it. The sentiments could either be positive, negative, or even neutral. And sentiment analysis is an algorithm-driven process to determine these sentiments from the customers' responses. The algorithm has access to a dictionary of words, each of them holding a positive, negative or neutral sentiment.

Sentiments are opinions, ideas, or thoughts based on emotions and show subjective impressions, not facts.

Sentiment analysis is the process of identifying and extracting sentiments from a piece of text using machine learning or natural language processing.

It can be used to determine what emotions a group of words convey, such as how positive or negative a person is.

Benefits Of Sentiment Analysis In Social Media

With social media becoming a vital marketing aspect for businesses of all shapes and sizes, decoding the customer's message and analyzing the brand's online buzz has become a new skill set. Here are some other use-cases and benefits of sentiment analysis in social media for business:

To Improve Customer Service

Just as a good relationship between a brand and its customer grows over time, so does its influence on business growth. A well-developed customer support system can help in strengthening such relationships.

Customer sentiment will never always be positive. And, negative feedback from customers isn't necessarily false. These mixed opinions may need systematic sorting, but implementing them will improve your overall customer service process in the longer run.

Sentiment analysis helps identify common customer grievances and enhance the business's capability to serve those needs.

The process starts with identifying the needs of individual customers and then identifying the business' operational shortcomings.

With the information acquired from the analysis, brands can improve their operations by revamping existing processes and systems, adding extra personnel, or building an entirely new infrastructure to serve customers better.

Improve Customer Service Using Sentiment Analysis

Image Source: Domino's Pizza Twitter

Plan Product Or Service Improvements

Analyzing customer talks, mentions and opinions is a treasure trove of data, especially when it comes to what you sell. Updating your products, improving their design, ideas bettering your services are all the results that can be derived from sentiment analysis. At times, this data can even yield new products and services ideas for your business to offer.

Plan Product Or Service Improvements Using Sentiment Analysis

Image Source: Frito-Lay Twitter

To Upsell

You might know about the benefits your product offers to your customers broadly. But there will always be a few products or their features most liked by the majority of your customer base. Capitalizing on this aspect will surely help your brand upsell a lot.

This can be easily done with positive-sentiment-based offerings. It means that you offer more of the same product or service that is extensively being used by existing customers.

This technique is effective for brands with excellent reviews on their products or services but is unsure of their brand perception among new customers.

For New Marketing Strategies

In today's competitive market, brands have to understand their target audience thoroughly. Market research coming from the customer care database can help uncover exactly how customers interact with the brand.

A study about customer behaviors and needs, how customers use a product or service, their preferences, demographics can all help a business formulate effective marketing plans for optimum sales.

Companies can use the results obtained by analyzing customers' sentiments on social media that could lead to a complete market-tailored solution. Sentiment analysis can also find the social network where your ideal target audience exists and interact with them.

Know The Brand Performance Of Your Competitors

Sentiment analysis can be used for monitoring sentiments related to your brand and for your competitors. Thus, you can learn what kind of products and services your target audience likes or dislikes about your competitors and adjust your business strategy accordingly.

Improve Crisis Prevention

It's better to prevent a crisis from happening than to deal with its disastrous aftermath. In such a case, sentiment analysis comes in handy. You can detect negative sentiments and avert a devastating crisis by monitoring real-time reviews or comments.

For instance, as soon as you detect a lot of negativity towards your services, you can assess the reason behind it and develop a strategy of how you can avoid such negative reviews to pile up the next time.

Companies should react quickly to changes in their customer behaviors. Analyzing what has gone wrong with a particular customer can help create better responses and prevent customer damage or loss caused by delayed action.

Improve Crisis Prevention Using Sentiment Analysis

Image Source: REESE'S Twitter

Types Of Sentiment Analysis

1. Standard Sentiment Analysis

Standard sentiment analysis is the most popular type. It studies and recognizes the expression and the tone of opinions about a product or service. It categorizes the subjective details as positive, negative, or neutral data.

Words like 'anger,' 'sadness,' and 'worries' are associated with negative sentiment, while words like 'happiness,' 'love,' and 'enthusiasm' are linked with positive sentiments.

Standard Sentiment Analysis

Image Source: Mathworks

2. Fine-Grained Sentiment Analysis

This type focuses on recognizing people's opinions with more precise output. The feedback gathered is ranked into 5 ratings, which are:

  • Very positive
  • Positive
  • Neutral
  • Negative
  • Very negative

Besides the mentioned common keywords, other traits such as feelings can also be used to measure whether the responses are positive or negative.

Fine-Grained Sentiment Analysis

Image Source: Quora

3. Emotion Detection

This type of sentiment analysis finds the customers' feelings within the text or responses provided.

The words or feedback are observed and associated with different feelings such as happiness, sadness, frustration, anger, and many more.

The downside faced here is the confusion caused by the emotion detection system that recognizes the various lexicons (list of emotions or words) used by the customers to express their opinions.

The example below describes how a customer uses the same word to portray both negative and positive perceptions of a product.

The word 'kill' can be used in both negative and positive remarks; 'Your products are killing the market!' and 'Your products are killing me!'

4. Aspect-Based Sentiment Analysis

Product reviews are often considered an excellent way to understand the customers' thoughts about a product or service. By discussing the specific aspect or detail of the product, the composed opinions of your customers provide valuable insights into the brands. For example:

'The sound coming from the speaker of XX Product is too low!'

The expression of this response sounds negative as it describes the product's characteristic of the product that is used for further improvement purposes.

Aspect-Based Sentiment Analysis

Image Source: Monkeylearn

5. Intent Analysis

Intent analysis understands the action underlying the opinions or reviews given by the audience. This sentiment analysis type provides opportunities to resolve your customers' issues or complaints related to the product or service. For example:

'I can't take it anymore! The XX application kept closing whenever I logged in. Can you help?'

Using this sentiment analysis type, it is very easy to detect customers' complaints and resolve problems.

Intent Analysis

Image Source: Digitalvidya

How Does Social Media Sentiment Analysis Work?

The three basic approaches to social media-based sentiment analysis are given below:

Rules-based Sentiment Analysis

This approach relies on rules a user creates and manages. To illustrate, you might specify that the word "great" would carry a positive sentiment, and the word "bad" would carry a negative one. When this rule applies, the phrase, "The service here is great!" will be marked as positive sentiment, and the phrase, "The service here is a bad one!" will be marked as negative sentiment.

A point of an advantage here is that it's fully customizable to your unique wants and needs.

Automatic Sentiment Analysis

This approach is a fully automated system and relies on machine learning. It's easier to set up and manage. If your audience's use of specific words or phrases changes, insert new contextual cues and keywords for the tool to work correctly.

Hybrid Sentiment Analysis

This sentiment analysis approach combines the best parts of both the methods above. Today, most tools in the market use this form of sentiment analysis to provide an optimal experience.

What Does Social Media Sentiment Analysis Measure?

Sentiment analysis is a complex process. It considers a variety of factors and sees how they all relate to each other. Here's the essential data that social media sentiment analysis takes to analyze the sentiment:

The Tone Of Comments

Every part of a customer's message, from word choice, abbreviations, punctuation marks, emoji, and the structure of sentences, can indicate the tone of the reviews.

Engagement

The qualitative measure of customer engagements is not always necessary as a large number of comments doesn't necessarily indicate positive sentiment. Angry customers would love to bash your comment sections with negative reviews and complaints. Hence, it's essential to analyze the sentiment of such engagement.

Brand Mentions

Users who tag your social handle in their posts either really love or hate you. Either way, it's essential to consider these mentions when analyzing their sentiments. Share of voice (that is, how much of the target market your brand takes up compared to your competitors) can also be analyzed using brand mentions.

Brand Mentions

Change In The Frequency Of Mentions And Quantity Of Engagement

When the frequency of your mentions or quantity of engagement on social media suddenly changes, it's a matter to consider.

Net Sentiment

Net sentiments give you a basic understanding of how you are faring online on social media. Just subtract the negative mentions percentage from the percentage of your positive ones. The higher the number, the better you are doing.

That's...a lot. But you don't have to measure all of these manually. A sentiment analysis tool such as Statusbrew will take care of everything for you and get the job done.

Sentiment Analysis Best Practices

Continuously Monitor Your Brand

You need to know what potential customers say about your brand on social media. Plus, how your marketing campaigns are affecting the perception of your brand.

The sentiment is a metric that needs continuous tracking. As you improve both your processes and products, opinions will change. Seeing these changes allow for better navigating the tumultuous waters of sentiment.

A brand tracking tool such as Statusbrew can help to monitor the positive/negative associations to your brand and provide an overall indication of your brand's health.

Continuously Monitor Your Brand

Choose Trackable Keywords Carefully

Part of carrying out a good sentiment analysis is being able to separate your keywords effectively. The keywords or phrases you want to target will depend on the kind of sentiment you wish to uncover.

For analyzing negative sentiment, you might wish to go for words like "bad," "worst," and "hate." On the other hand, for the tracking of positive sentiment, target words such as "love," "like," and "favorite."

It's also helpful to think about what your audience might be saying about you on social media. Think about what kinds of things they post and what they usually search for. Would any current trends affect any of this?

Once you have a good idea of how your target audience interacts online and the kinds of content they want to see, it will help you target a few niche keywords. These will help create creative and informative posts and tweets that generic keywords might not have found.

Dig deeper into your brand sentiment and find some negative posts or tweets that aren't liked by your audience. You might think that your customer service is on point, although your customers might actually disagree with you.

Choose Trackable Keywords Carefully

Keep An Eye On Your Competitors

You can also perform a brand sentiment analysis of your competitors. You can do a very quick search to flag up what is being said about your competitors too. The idea here is to use the competitors' drawbacks or weaknesses for your brand's advantage. If there is a product or feature that your target customers are suggesting to your competitors, you can incorporate that feature into your product or brand and gain a competitive edge.

One of the first things that many brands do is compare positive and negative sentiment. Does it look like consumers are generally more positive or negative to a similar brand to you? If so, why is that?

It's also worth seeing what kind of negative or positive points are being said about competitors. For instance, if your brand focuses on sports apparel with a USP of being waterproof, it could be worth noting how consumers feel about this feature in competitor products.

So, when putting your keywords into a sentiment analysis tool, add in words such as "waterproof," "weather-resistant," and "great in the rain." This will show sentiment around this particular product feature. If you notice that your competitors' waterproof sports apparel are well in demand, you know which product to capitalize on.

Adapt Your Message

The sentiments are always in flux. Just because your customers talk positively of your brand relatively high right now, there's no guarantee that they will continue to do so in months or years to come. Hence, sentiments are something that you should regularly track and, whenever necessary, adapt to them accordingly.

If your brand is showing more negative sentiment, don't panic. You still have a chance to act upon it and improve. But don't ignore the negative sentiments completely. Remember to work on them, especially if your customers highly talk about them.

If you want to do something proactive against any bad response, you can always work to drown out the negativity.

Adapt Your Message With Sentiment Analysis

Image Source: R Bloggers

Ask For Customer Feedback

Never shy away from asking your customers for their feedback. This is valuable data that can be added to your sentiment analysis.

Many brands find success when using customer surveys, as people are more likely to say anything negative when able to do so anonymously. All of this feedback that people aren't willing to share in person or on a public platform provides you with even more data to be analyzed. You will be going a lot deeper than simply doing sentiment analysis based on what's being said on socials.

What Are The Main Issues With Sentiment Analysis?

Sentiment analysis is not perfect and does come with some expected quirks and problems. Below are given are some aspects that sentiment analysis fails to catch:

Context Of Language

When sentiment analysis scans text, it interprets the words without the ability to look into context or backstory — which can alter the result.

Sarcastic Content And Humor

When scanning text containing sarcasm, the tool will often produce an incorrect result due to a misunderstanding of sarcasm.

Compare And Contrast

Sentiment analysis can fail when a user comments on a change or alteration in a product or service or while comparing products from different suppliers. Because this is based on context, it rarely produces a truly accurate result.

The Subjectivity Of Neutrality

Of all sentiments, neutrality is the most difficult tone for the tools to detect because it's very subjective. These results are typically weighted as neutral as opposed to a specific category.

You should spot-check these types of results to ensure your analysis is categorizing things properly.

Make The Best Out Of Sentiment Analysis With Statusbrew!

Statusbrew offers sentiment analysis for all your social conversations. Statusbrew instantly marks each conversation as positive, negative, or neutral with highly trained AI for human interaction and language. It also supports 12 languages.

In the Engage screen, the conversation detail section contains the sentiment icon, which denotes the detected sentiment for each conversation. You can change the sentiment manually by clicking on the sentiment icon if you feel like it.

Make The Best Out Of Sentiment Analysis With Statusbrew

In fact, you can even quickly filter conversations based on their sentiment in the Engage inbox. And that's not all; Statusbrew even offers insightful reporting on the sentiment of the conversations.

Want to discuss more? Snag a free trial today!

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Rushali Das

Rushali is a content writer at Statusbrew. She is fond of writing content that compels people to slow down and look more closely to discover the unknown stories.