Authors :
Tinashe Rufu; Tawanda Mudawarima; Stanford Mavuchi
Volume/Issue :
Volume 8 - 2023, Issue 8 - August
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://tinyurl.com/2nrexxny
DOI :
https://doi.org/10.5281/zenodo.8344308
Abstract :
The widely anticipated use of natural language
processing (NLP) and machine learning is sentiment
analysis (SA). The introduction of Web 2.0 resulted in this
industry expanding significantly. People have long used
the Internet as a forum to share their thoughts, feelings,
and judgments about things, other people, and life in
general. As a result, the Internet is currently a vast supply
of humorous written data that expresses opinions. The
automatic classification of opinionated writing as being
negative, positive, or neutral is a crucial function of
sentiment analysis. This study compared methods,
techniques used in sentimental analysis, and its application
in recommendation systems. Since the advent of
recommender systems, this field of study has become
increasingly important.
Keywords :
Sentimental Analysis, Recommendation System, Emoji Analysis, Sarcasm Detection, Machine Learning.
The widely anticipated use of natural language
processing (NLP) and machine learning is sentiment
analysis (SA). The introduction of Web 2.0 resulted in this
industry expanding significantly. People have long used
the Internet as a forum to share their thoughts, feelings,
and judgments about things, other people, and life in
general. As a result, the Internet is currently a vast supply
of humorous written data that expresses opinions. The
automatic classification of opinionated writing as being
negative, positive, or neutral is a crucial function of
sentiment analysis. This study compared methods,
techniques used in sentimental analysis, and its application
in recommendation systems. Since the advent of
recommender systems, this field of study has become
increasingly important.
Keywords :
Sentimental Analysis, Recommendation System, Emoji Analysis, Sarcasm Detection, Machine Learning.
