Help brand insight -- Analysis of consumers' emotional behavior

What is emotional analysis ？

With the emergence of social networks and digital marketing , Consumers' evaluation of products and brands has attracted more and more attention . Online user feedback （ For example, product evaluation 、 Social media reviews and questionnaires ） It contains a lot of valuable data . Through these data , You can understand consumers' views on your products , And their satisfaction with all aspects of the product , The most important one is , How to respond to their feedback . Emotion analysis can help us further understand these aspects , It is a practical tool for analyzing consumers' emotions and opinions .

This paper will focus on the concept of emotional analysis 、 Main working principle and its importance to online business , And machine learning （ML） And natural language processing （NLP） The role played in it .

The definition of emotional analysis

Sentiment analysis （ Also called opinion mining ） It's an automated method , Used to identify 、 extract 、 Quantify and study consumer perceptions of brands 、 Attitude and evaluation of products or services . This method is mainly based on NLP、 Computational linguistics 、 Machine learning and other tools .

Through this automated method , Brands can understand public opinions , And carry out detailed market research and Evaluation manipulation . All these measures can help enterprises adjust their products according to consumer needs .

  Types of emotional analysis

The purpose of emotion analysis model is to determine consumers' Emotional polarity 、 Emotional type 、 Expression of intention （ Interested or not 、 Willing to buy or unwilling to buy ） And urgency . Some of the most commonly used types of emotional analysis include ：

  Fine grained emotional analysis  

If you want emotional analysis to be as accurate as possible , Additional polarity classifications can be added , for example ：

• Very negative
• negative
• Neutral
• positive
• Very positive  

These categories correspond to the five-star rating , Very positive, equivalent to five stars , Very negative is equivalent to one star .

  Emotion recognition  

This type is designed to identify depression 、 Happy and other emotions and feelings . Many emotion recognition methods are based on thesaurus , That is, the use of a vocabulary system with emotions .

  Aspect level emotion analysis  

When brands analyze the emotions behind a paragraph , Want to know that consumers take a positive 、 Negative or neutral emotions discuss what characteristics and aspects of their products .

for example , In the following comments ：“ The camera of this phone is worse than I expected ”, This is a negative evaluation of the specific functions of the product .

Why is emotional analysis important ？

Because emotional analysis uses automated methods , Therefore, enterprises can timely sort out and analyze a large number of emotions behind social media conversations and evaluations .

in general , Basic emotion analysis promotes the collection and measurement of social data in the following aspects ：

• Collect a lot of data
• Real time analysis
• Unified analysis standard

What is the working principle of emotion analysis ？

There are mainly three kinds of emotion analysis algorithms to realize emotion analysis and opinion mining ： rule-based （ Based on Thesaurus ）、 Automatically （ machine learning ） And mixing .

  A rule-based approach  

Most of the time , The rule-based emotion analysis algorithm relies on artificial rules to judge the emotional polarity reflected by the text 、 Subjectivity and emotion . These rules are based on different NLP Emotion analysis technology , This kind of technology was originally developed from the field of computational linguistics , Including part of speech tagging 、 participle 、 Stem extraction and other technologies .

In this way , Emotion analysis uses the emotion analysis data set , for example ： A large number of adjectives （ Pretty good 、 great 、 too bad ） And phrases （ Quality service 、 A bad movie ）, And the programmer manually assigns specific scores to these words .

  be based on NLP and ML Emotional analysis  

Automatic emotion analysis method is based on machine learning algorithm , Train for the input data .

Machine learning is in NLP And text emotion analysis , Enhance and automate low-level text analysis （ Such as part of speech tagging 、 participle 、 Emotional recognition, etc ）.

The beginning of the learning process is semi automated . The algorithm is based on the data provided , Learn how to recognize and analyze emotions .

The challenge of emotional analysis

Although the emotion analysis model is becoming more and more perfect and accurate , But to be the ultimate solution , There are still many obstacles to overcome .

  context  

All spoken and written language is in a specific environment 、 At a certain point in time 、 Expressed by a specific person to others . let me put it another way , They all have context . The problem lies in , If context is not specifically mentioned , The machine cannot recognize the context .

  Satire and irony  

People often use positive words to express irony and irony . Without knowing the context , It may be difficult for machines to understand the emotions in these expressions .

  emoticon  

according to Guibon And other scholars' papers , There are three types of emoticons ：

• Western emoticons , Contains oneortwo characters , for example ： :0
• More complex Eastern emoticons , for example ： (°レ°) 
• Unicode Emoticons .

Analyzing emoticons and characters is as critical as analyzing words and other phonetic components .

Application of emotion analysis

Emotion analysis can be applied in many fields , Including brand control 、 market research 、 Social media manipulation . Next Oxylabs Let me show you some common application scenarios .

  Brand monitoring  

By analyzing blogs 、 Forum 、 Emotions in news reports and other sources of information , It helps to understand how consumers treat you brand Your views and feelings . Get measurable statistics on consumer satisfaction , It helps to understand the development trend of your brand image , And how it relates to the brand image of its competitors . 

  market research  

Whether you are studying competitors or exploring new markets , Emotional analysis in any market research Both are beneficial . for example , You can study online reviews of competitors' new products , Find out their advantages and disadvantages , Learn from it .

Consumer services  

Consumers hope to communicate with brands in a timely manner 、 Stress free interaction . The way companies provide products and services is as important as the products and services themselves .

In terms of consumer services , You can use consumer sentiment analysis , Arrange the processing order of the consumer inquiry according to the urgency of the consumer and the subject of the transaction , And guide it to the corresponding department . thus , It can communicate with consumers more efficiently , It also ensures that the matters that pay most attention to timeliness are solved immediately .

summary

As consumers publish more and more comments and opinions on the Internet every day , The importance of processing these data and drawing conclusions in time is becoming increasingly prominent .

Through emotional analysis , You can know how consumers feel about your brand and products , And how to improve your service . Thanks to natural language processing and progressive machine learning technology , Emotion analysis can serve a variety of scenarios , Including brand manipulation and Market Research . If you want to know more , You can see Our article , You can also visit our at any time Official website , We will do everything we can to help .