Sentiment analysis: deciphering opinions through data

Sentiment analysis is a technique for natural language processing that It is used to classify qualitative data as positive, negative or neutral. This analysis can also be refined further and made more nuanced. The main objective of sentiment analysis for businesses is to find out what customers generally think regarding its services and brand, as well as to identify any potential weaknesses that need to be addressed.

Since the widespread adoption of artificial intelligence, sentiment analysis has become a faster and more comprehensive task. An AI system can process a vast amount of qualitative textual data from customer reviews and classify it into the categories we specify in a matter of seconds, with almost flawless accuracy.

Types of sentiment analysis

Sentiment analysis covers various aspects, such as the overall assessment of a service, the detection of customer emotions, and the intentions a customer may have regarding a product. What is analysed and interpreted will depend on the criteria that each company has identified as relevant to its business strategy.

We can therefore identify different ways of carrying out sentiment analysis:

Polar analysis

It is the simplest and best-known form of analysis. In this method, customers’ opinions are gathered and they are gradually sorted into polarised categories of “bad” or “good”. Clearly, this two-dimensional categorisation would be far too limited for an analysis based on it to bear fruit.

Generally speaking, in a polar analysis it is advisable to include several levels of satisfaction in order to distinguish more subtle nuances in customers“ opinions. Thus, some categories might include ”very positive“, ”positive“, ”neutral“, ”negative“, ”very negative“, ”perfect“, ”terrible’, etc.

An example of data collection intended for subsequent polar sentiment analysis would be the ratings from one to five stars, which will subsequently be reinterpreted on a scale of greater or lesser customer satisfaction.

Identifying emotions

Although the feedback we process is usually in text form, and we cannot see the face or hear the voice of the customer who wrote it, we can often sense the emotions conveyed by their words.

That is why many sentiment analysis tools rely on artificial intelligence algorithms who work on the a lexicon of emotions for categorising ratings of customers’ various emotions, such as happiness, frustration, anger, anticipation…

Whilst artificial intelligence is advancing by leaps and bounds, everyone has their own particular way of expressing their feelings in writing, so AI might sometimes get confused.

For example, it is not the same thing to describe a dish as “terrible” as it is to describe it as “terribly delicious”. A human being would have no hesitation in correctly identifying the emotions conveyed by one comment or the other, but an AI might hesitate.

Sentiment analysis by facets

This is simply an extension and elaboration of a general analysis of sentiments. A company’s services and products cannot be assessed in isolation. In other words, Customers may place different levels of importance on certain aspects of their shopping experience. For example, a customer may be satisfied with a product they have received, but may have complaints about the company’s customer service.

That is why a sentiment analysis by facet is necessary in order to interpret the data in a more targeted way and draw more insightful conclusions about all the possible aspects involved in the customer experience.

Multilingual analysis

Whilst this may not seem particularly important for companies operating at a very local level, it certainly is for the majority of businesses which, inevitably, carry out its business activities in a globalised world. That is why a comprehensive sentiment analysis tool must incorporate multilingual linguistic corpora and have been trained on the languages of interest to users.

Benefits of sentiment analysis

Sentiment analysis enables companies to find out with certainty what customers think about any aspect of the services or products they are offering. It is a quick way of processing a large number of opinions and seeing them summarised concisely in easy-to-interpret metrics and charts.

Using these assessments, they identify areas for improvement in which customers have expressed significant frustration or anger. For example, a company selling on Amazon might identify anger amongst a large number of customers who receive their product with damaged packaging.

It also serves to to strengthen and nurture the aspects that customers value highly. In other cases, this qualitative data also reveals unexpected insights that may be useful to the company in terms of innovation, going beyond mere sentiment analysis.

One undeniable advantage of AI-powered sentiment analysis is speed depending on the method used. Can you imagine having to manually categorise 5,000 product reviews? It would take an age. Nowadays, with an AI system, all this data would be properly processed and categorised in the blink of an eye.

Furthermore, a sentiment analysis system would also be useful for to identify urgent situations of dissatisfaction real-time customer feedback. This is particularly true of customer complaints on social media, which are becoming increasingly common and reflect a very visible level of frustration that could damage the company’s brand image. In this way, we can easily identify this problem and provide an effective solution to appease the customer.

Sentiment analysis on social media

Sentiment analysis is very useful and is frequently used in sales and marketing, particularly on social media. Many social media platforms and tools offer the option to to gather mentions of a brand or product, even if they do not tag the company directly.

Whilst counting the number of mentions of a brand can help to determine how much buzz we are generating, It tells us nothing about the content or quality of these mentions. That is why this set of mentions must be subjected to sentiment analysis in order to extract meaningful data. 

Similarly, this real-time social media analysis helps us to dealing with crises on social media, that is to say, situations in which a company faces very harsh criticism through public comments. In this way, community managers can fall back on their crisis management plan and pull strings to provide the user with a swift solution and prevent further damage to the brand.

Example of sentiment analysis

Let’s suppose we want to analyse the sentiment of a film review. Here is the review:

«The film was absolutely brilliant. The visual effects were stunning and the plot was gripping. However, the pace of the film was a bit slow in places. Overall, I really enjoyed watching it.»

To analyse the sentiment of this review, the system would identify keywords that denote positive or negative sentiments. For example:

• Positive words: incredible, stunning, captivating, I enjoyed it.
• Negative words: slow.

Based on these keywords, we could conclude that the review has a predominantly positive tone, with a slight criticism of the film’s pacing.