Sentiment analysis: how AI recognizes moods

Sentiment analysis is a natural language processing method that aims to identify the tone or attitude expressed in a text. It is used to automatically evaluate opinions expressed on social networks, customer reviews or surveys.

Sentiment analysis: why do we need it?

THE success or failure of a brand does not depend only on direct sales, which can fluctuate in the short term, but also on thecustomer opinion. What matters most is how potential customers talk about the brand, whether they have already purchased the product or not.

• Is the brand in tune with the times?
• Does the target audience perceive the brand positively, or on the contrary negatively?
• On the contrary, is it completely ignored?
• Is the brand popular with influencers?

So many essential questions that a company must answer based on a regular and targeted monitoring social networks. Sentiment analyzes are also used by stock market experts, in order to anticipate theevolution of stock prices from purchasing behavior and the general climate among investors.

Sentiment analysis, also called opinion mining Or sentiment analysis in English, is based on the automated evaluation of user comments to determine whether a text expresses a positive or negative opinion. It is based on methods of text mining (see also data mining), that is to say the automatic analysis of texts written in natural language.

Among the main challenges of this discipline, we can cite:

• Natural language is not reduced to lists of positive and negative words: its meaning varies depending on the context.
• THE analysis methods which are based on a thematic dictionary of positive or negative words only provide a very approximate vision.
• The frequency of appearance of certain words associated with a positive or negative evaluation of a product is not necessarily representative.
• On social networks, opinions are not not always expressed according to grammatical rules.
• Depending on the target, we observe variations in the use of language, for example with slang or youth language.

These difficulties can be illustrated through two examples of customer comments:

To carry out effective sentiment analysis, we are therefore increasingly using tools based on artificial intelligence. The methods of machine learning make it possible to train tools capable of taking into account the target group and the context of the product analyzed. In the long term, this improves the quality of results.

Sentiment analysis: what is the goal?

Its main function is to draw up a picture of the general feeling with regard to a product or brand within a given target audience. To do this, for example, we can collect all product reviews on the sites of major online stores or search publications on the subject on Facebook, Twitter and other social networks.

This type of analysis must take into account the emotions that permeate the text and understand what the author really wants to say.

However, this is not a tool intended to respond individually to reviews or product ratings. In such cases, it is best to have someone write a personalized response.

Sentiment analysis: what are the advantages?

Sentiment analysis offers businesses many benefits in the areas of marketing, customer service, and brand perception. The automated evaluation of large quantities of text makes it possible to analyze and exploit customers' opinions, attitudes and emotions in a targeted manner.

✓ Early detection of negative reviews: professional text analyzes make it possible to quickly grasp trends within a target. Companies can thus react in time with appropriate measures, for example via adjusted communication or targeted campaigns.

✓ More targeted marketing: Thanks to the analysis of customer comments, it is possible to identify positive experiences. This information can be used to offer personalized advertising or promotional offers, ideally where the target is most active.

✓ Strengthening loyalty: Better understanding your customers allows you to create more tailored offers and meet their needs. This strengthens loyalty and sustainably increases satisfaction.

✓ Reputation management: Opinion mining helps track public perception of the brand. Potential crises can thus be detected quickly and reputational risks reduced.

Sentiment analysis: when to use the method?

Sentiment analysis has many applications when opinions, evaluations or states of mind play a role. Companies use it in particular to better understand the behavior of their customers and react more quickly to trends. Here are some common areas of use:

• Advertising campaigns on social networks : Potential customers here react directly to company messages and sometimes communicate with each other, often more honestly than they would with the brand itself.
• Adjusting campaigns : when a negative trend appears or a product is perceived incorrectly, campaigns can be quickly adapted and then re-evaluated.
• Reactions to product or brand developments : after the launch of a new improved version of a product or following visual changes in the brand identity, sentiment analyzes make it possible to evaluate the impact on customer satisfaction and on the behavior of possible new customers.
• Identification of relevant content : in addition to filtering spam, it is also a matter of excluding from analysis texts that have only an indirect link with the product.
• Ranking of customer returns : Relevant brand reviews can be categorized, for example to distinguish genuine product reviews from customer service or packaging reviews, which often contain more negative terms.
• Measuring campaign success : this approach makes it possible to measure the success of a marketing campaign, for example when keywords or advertising slogans appear frequently associated with positive terms in the comments.

Simple example of a sentiment analysis

Google Natural Language is a programming interface (API) which notably offers simple sentiment analysis methods and can be integrated into proprietary software. Google offers the possibility to any user (not just developers) to test this API. Simply copy a text into the API input field to access different text analysis options, including sentiment analysis.

Each sentence is analyzed separately and given a score between -1 And +1 : the first corresponds to a very negative feeling, the second to a very positive impression. The scores for the different sentences are then added together, and the total score is then interpreted using a predefined scoring grid.

The following example is based on a fictitious review about a kettle. The result highlights the limits of automatic text analysis : the lowest rated sentence contains the negative expression “I didn’t even imagine”. However, placed in context, it actually expresses a compliment.

This type of turn of phrase remains relatively rare in opinions (just like irony), an analysis of sentiments, even basic, nevertheless allows us to identify a general trend once we have a large volume of texts.

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Sentiment analysis: what tools are there?

Besides Google's Natural Language API mentioned above, there are other professional analysis tools capable of analyzing large quantities of text. When choosing one, you must ensure that the tool supports the French language and contains word lists and databases of standard expressions compiled by native speakers. Each language, especially in its colloquial register, has its own subtleties, which an automatic translator cannot reproduce without distorting the tone of the text.

Hootsuite

AI-powered sentiment analysis in the dashboard Hootsuite allows you to automatically evaluate the main social networks, news portals, blogs and known forums in order to determine the general perception of Internet users towards a brand or product. The comments taken into account for the analysis can be filtered according to different keywords and target groups.

However, the tool is not limited to that: it also offers other functionalities useful to businesses. For example, it includes AI assistance for content creation and recommends the best times to publish.

IBM Watson Natural Language Understanding

IBM Watson Natural Language Understanding is a powerful AI-based text analysis tool capable of detecting sentiments, emotions, keywords and themes. It allows detailed analysis of content in several languages. The API can be flexibly integrated into existing systems and provides precise information about the tone and intent of texts. A free trial version is available to test the IBM tool.

Clickworker

Clickworker takes a different approach. Here, a large network of users works on the texts via microtasks. Instead of an automatic analysis, we obtain an evaluation of the tone of the content based on targeted and simple questions.

The advantage of this method is obvious: human intelligence makes it possible to interpret feelings globally without being limited to the connotation of isolated words. Thanks to the intervention of three to five clickworkers by text and a majority-based decision system, the results offer a high degree of reliability.