What is sentiment analysis and how does it work?

Sentiment analysis is a natural language pro­cess­ing technique aimed at iden­ti­fy­ing the sentiment or attitude in texts. It is used to au­to­mat­i­cal­ly evaluate opinions on social media, customer reviews, or surveys.

Why is sentiment analysis needed?

For the success or failure of a brand, not only the direct sales figures, which can change in the short term, are decisive, but also customer opinions. It’s primarily about how potential customers talk about the brand—re­gard­less of whether they have already bought the product or not.

• Does a brand fit the current trend?
• Is the brand perceived pos­i­tive­ly or neg­a­tive­ly by the desired target group?
• Is the brand com­plete­ly ignored?
• How is the brand received by in­flu­encers?

These are important questions a company should regularly address through targeted mon­i­tor­ing of social media channels. Sentiment analysis is also conducted by stock market spe­cial­ists to estimate the course of stock prices based on investor purchase behavior and overall sentiment.

How does sentiment analysis work?

Sentiment analysis, also known as “emotion detection,” is based on the automated eval­u­a­tion of user comments to determine whether a text is intended to be positive or negative. It uses methods of “text mining” (see also data mining), which involves the automatic analysis of texts written in natural language.

The main chal­lenges in sentiment analysis include:

• Natural language is not simply a list of positive and negative words — its meaning shifts depending on the context.
• Analysis methods that rely on pre­com­piled, topic-specific dic­tio­nar­ies to identify positive or negative terms offer only a very rough picture.
• The frequency of sup­pos­ed­ly positive or negative words says little about a person’s actual opinion of a product.
• On social media, opinions are often expressed in ways that do not follow standard English grammar.
• Depending on the target audience, language patterns such as slang or emerging trends can also influence in­ter­pre­ta­tion.

These dif­fi­cul­ties can be il­lus­trat­ed by two different customer reviews:

For suc­cess­ful sentiment analysis, ar­ti­fi­cial in­tel­li­gence tools are in­creas­ing­ly being used. Machine learning methods help train tools that closely un­der­stand the target audience and the en­vi­ron­ment of the product to be analyzed. In the long run, this improves the quality of the results.

What is the purpose of sentiment analysis?

The most important task of sentiment analysis is to determine a general sentiment about a product or brand within a defined target audience. It’s useful to search through product reviews on your own website or major online stores, as well as the­mat­i­cal­ly relevant posts on Facebook, Twitter, and other social networks.

Sentiment analysis aims to detect the emotions behind the written text and also capture what the author of the text actually meant.

However, sentiment analysis is not a tool for re­spond­ing to in­di­vid­ual opinion pieces or product reviews. In such cases, it is better for a human to write a personal reply.

What are the ad­van­tages of sentiment analysis?

Sentiment analysis offers busi­ness­es numerous ad­van­tages in the areas of marketing, customer service, and brand per­cep­tion. The automated eval­u­a­tion of large text quan­ti­ties allows for targeted analysis and use of customer opinions, attitudes, and emotions.

✓ Early detection of negative customer sentiment: Pro­fes­sion­al text analyses enable the quick iden­ti­fi­ca­tion of sen­ti­ments within a target group. This allows busi­ness­es to respond promptly and coun­ter­act with ap­pro­pri­ate measures, such as adjusted com­mu­ni­ca­tion or targeted campaigns.

✓ More targeted marketing: By analyzing customer comments, positive customer ex­pe­ri­ences can be iden­ti­fied. This in­for­ma­tion can be used to offer per­son­al­ized ad­ver­tis­ing or pro­mo­tions—ideally exactly where the target audience is active.

✓ Strength­en­ing customer loyalty: Un­der­stand­ing your customers better allows for creating more tailored offers and ad­dress­ing their needs. This strength­ens customer loyalty and increases sat­is­fac­tion in the long term.

✓ Rep­u­ta­tion man­age­ment: Sentiment analysis helps keep track of the brand’s public per­cep­tion. This allows for early crisis detection and minimizes rep­u­ta­tion risks.

When is sentiment analysis used?

Sentiment analysis is used in many areas where opinions, reviews, or sen­ti­ments play a role. Companies, in par­tic­u­lar, use it to gain insights into customer behavior and respond more quickly to trends. The following areas of ap­pli­ca­tion are par­tic­u­lar­ly popular:

• Ad­ver­tis­ing campaigns on social networks: Here, potential customers respond im­me­di­ate­ly to the company’s state­ments and sometimes even com­mu­ni­cate with each other—often much more honestly than they would with the company.
• Adjusting campaigns: If a negative sentiment emerges or a wrong im­pres­sion of the ad­ver­tised products arises, the re­spec­tive campaigns can be adapted on short notice and then re-evaluated.
• Response to product or brand ad­just­ments: Even after a new, possibly improved edition of a well-known product or visual changes to the brand, sentiment analysis is helpful to assess how the re­align­ment impacts customer sat­is­fac­tion and possibly the behavior of new customers.
• Finding relevant content: Besides filtering out spam, it is also about finding texts and excluding them from the analysis if they are only in­di­rect­ly related to one’s own product.
• Sorting feedback: Relevant comments on one’s brand should be cat­e­go­rized or filtered according to further criteria—for example, whether they are actual product reviews or if criticism is more about customer service or packaging, thus con­tain­ing many negative terms.
• Measuring success: Sentiment analysis can be used to measure the success of marketing campaigns, for instance, when terms or phrases from the current ad­ver­tis­ing appear fre­quent­ly in comments along with positive words.

Example of a simple sentiment analysis

Google’s Natural Language API is a pro­gram­ming interface that, among other things, supports simple sentiment analysis methods and can be in­te­grat­ed into your own programs. Google allows everyone, not just software de­vel­op­ers, to test this API. You only need to copy a text into the input field of the Google Natural Language API and you will receive various options for text analysis, including the “Sentiment” selection.

Each sentence is evaluated in­di­vid­u­al­ly and receives a rating between -1 and +1, with -1 rep­re­sent­ing “very negative” and +1 rep­re­sent­ing “optimal.” A cu­mu­la­tive result for the text is derived from the ratings of in­di­vid­ual sentences according to a pre­de­fined hierarchy of values.

In the example below, we use a fictional review of a kettle to il­lus­trate the lim­i­ta­tions of automatic text analysis. The lowest-rated sentence includes the negative phrase “no idea.” Yet, when the entire review is read in context, it becomes clear that the user is actually ex­press­ing praise in that part of the text.

Since such ex­pres­sions and irony in reviews are ex­cep­tions, even a simple sentiment analysis can be suitable to at least obtain a general mood picture from large amounts of text.

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What tools are available for sentiment analysis?

In addition to the afore­men­tioned Google Natural Language API, there are other pro­fes­sion­al analysis tools that can evaluate large amounts of text. It is important to ensure that the tool contains word lists and databases developed by native speakers with typical ex­pres­sions in semantic contexts. Every language, es­pe­cial­ly when con­sid­er­ing col­lo­qui­al language, has its own nuances that an automatic trans­la­tor cannot capture without dis­tort­ing the sentiment of a text.

Hootsuite

The AI-powered sentiment analysis in the Hootsuite dashboard au­to­mat­i­cal­ly evaluates all major social media channels, news sites, popular blogs, and forums to determine the general sentiment of internet users toward a product brand. The comments used for the analysis can be filtered by various keywords and typical de­mo­graph­ics.

In addition to sentiment analysis, the tool includes other features useful for busi­ness­es. It offers AI as­sis­tance for content creation and suggests the best times for posting. Plans start at $99 per user per month.

IBM Watson Natural Language Un­der­stand­ing

IBM Watson Natural Language Un­der­stand­ing is a powerful AI tool for text analysis that can detect sentiment, emotions, keywords, and topics. It enables detailed content eval­u­a­tion in multiple languages. The API can be flexibly in­te­grat­ed into existing systems and provides detailed insights into the sentiment and in­ten­tions of texts. You can try the IBM tool with the free trial version.

Click­work­er

Click­work­er takes a different approach. Here, a large network of users works on texts through micro-jobs. This way, you get a sentiment picture through targeted simple questions instead of an automatic text analysis.

The benefit of this approach is clear: Human reviewers can assess the sentiment of a text in full context, rather than relying solely on the con­no­ta­tion of in­di­vid­ual words. With three to five Click­work­ers eval­u­at­ing each text and results de­ter­mined by majority vote, the findings are highly reliable.