Increasing the popularity and use of social networks such as Facebook, Instagram, and YouTube, which enables users to share their information and comments with others, requires a new tool for processing content generated by users. On most sales and promotion sites, there is a scoring section on the features of a product. But it is used to create a section of a questionnaire. The problem with the questionnaire is that, firstly, all aspects of designing a product may not be considered by the designer of the questionnaire, and secondly, people prefer to express their opinions in a few sentences, rather than filling out long-time questionnaires.

The purpose of this research is to provide a solution that, by using the opinion mining, firstly, without the need for a questionnaire, can create an automatic scoring system for mobile phone product features, and secondly, classifying different people’s comments into three groups: “positive opinion”, “negative opinion” and “neutral comments”.

The present study was conducted using 1000 English-language comments on the Instagram social network for the Samsung Galaxy S8. In the present study, the “Hu and Bing Liu” dictionary has been used and 10 features related to a mobile phone such as “camera” with their synonyms are defined. The results show that the combination of the use of the dictionary-based method and SVM machine learning algorithm is successful.