A Business Intelligence model to analyze consumer opinions on social networks using machine learning techniques

In recent years, social networks have become a rich platform of information and data shared by users in almost all sectors of society. Therefore, the widespread use of the social network generates a huge amount of unstructured social data that must be analyzed and well structured. It is for this purpose that the sentiment analysis has received considerable attention. Sentiment analysis is presented as a text organization used to classify the feelings and opinions expressed by the user in different ways (negative, positive, neutral, favorable, unfavorable, etc.).Due to the massive amount of data available on social networks, sharing opinions, reviews, comments, and suggestions, it is necessary to extract, analyze, and consolidate for improved decision-making. Sentiment analysis presents an effective and efficient opinion of consumers in real-time, which can greatly affect business decision-making. To solve this problem sentiment analysis and machine learning techniques have been united because machine learning models can be beneficial in the field of business intelligence.This article proposes a BI model to observe in real-time the satisfaction and the reviews of consumers on a product or service on social networks, notably Facebook and Twitter, based on machine learning techniques.