Identifying user preferences is a complex operation, which makes its automa-tion
challenging, and existing recommendation systems that rely on one of the parameters …

Sentiment analysis is a technique for identification of expression, mind-set, or feelings of
users and classifies as negative, positive, favorable, unfavorable, etc. from a piece of text in …

Recommender systems based on sentiment analysis become challenging due to the
presence of enormous data available over the internet. With the lack of proper data cleaning …

Deep learning is a methodology applied across many fields. User comments are important
for recommender systems because they include various types of emotional information that …

Sentiment Analysis is a method of analyzing text and extracting opinions from it. It's also
known as emotion or opinion extraction, and it's part of the machine learning as well as data …

Customer product reviews play an important role in the customer's decision to purchase a
product or use a service. Providing a useful suggestion of products to online users to …

Expert assessments with pre-defined numerical or language terms can limit the scope of
decision-making models. We propose that decision-making models can incorporate expert …

Social networks and media have gradually grabbed significant time from people's lives to
share and communicate information. Moreover, these social platforms also act as an …

The advancement in technology accelerated and opened availability of various alternatives
to make a choice in every domain. In the era of big data it is a tedious and time consuming …

Recommender systems have been applied in a wide range of domains such as e-
commerce, media, banking, and utilities. This kind of system provides personalized …