According to Gupta (2018), opinion mining (OM), also known as sentiment analysis (SA), is the computational study of people's attitudes, opinions, and feelings regarding an object. It identifies the sentiment expressed in a text then analyzes it whether it is subjective or objective.

To analyze text sentiment of the review comments of given samples of data conducted to the TUP Students.

Utilize the TUP freedom wall and get dataset from it.

For the parents - The parents now have an assurance that students’ comments and voices are not misheard or misinterpreted in the reviews regarding student-university relationship.

Aims to take sample data of TUP student reviews accumulated through online scraping of the page.

Individuals are becoming more and more willing to share their opinions, beliefs, and lifestyle preferences in today's environment where vast amounts of information are available online. Students spend most of their time in school and tend to express their sentiments in life.

The study will be focusing on students’ opinion about Technological University of the Philippines (TUP).

For the students – The current and future students at the university can actively address their sentiments in the following reviews, student-university conversation and trust the system provided that their voice is transparently delivered using supervised machine learning.

To take advantage from the rise of software operation as a replacement to traditional physical tallying to overall quality of life improvement.

In order to provide services with outstanding quality, organizations must consider the their target's opinion, subjective as they may be, onto their whole structural formula on provision, which is why the study aims to provide additional knowledge to the behavioral patterns of the students from TUP.

As Technological
University of the Philippines (TUP) is a state university with
recognized excellence in engineering and technology
education. It is good to keep track of comments and reviews to
learn and improve quality and policies of the school.

Utilization of the students from Technological University of the Philippines (TUP).

For future researchers - The study would provide very productive progress in putting student-university relationships in more priority. Future researchers may take use of this research and integrate an advanced progressive system that would be used for the current and future generations.

For the professors – The TUP professors and other
professional academics are to be able to add accuracy to the reaction and response of certain student-university conflicts/conversations regarding societal issues and/or regulations. In addition, it will help professors address student morale and help in terms of what sentiments students provide in the obstacles of
their education.

Text analysis for underlying sentiment and not any other emotional analysis i.e., intent analysis.

Make use of Supervised machine learning for natural language processing and text analysis for the accumulated sample data

For the administrators – Administrators would be provided better accurate responses and more detailed expressions in their overall managed students.

Utilization of API to provide solutions on the said problems after performing Sentiment Analysis from the collected bag of words on SA.

This helps the university provide its students with the competitive advantage of a healthy learning environment and it is crucial for student satisfaction and dedication to affiliation with the university.

Since sentiment studies are a type of data mining that may track public sentiment toward numerous topics. It serves as the foundation for techniques in computational linguistics, biometrics, machine learning, and natural language processing. TUP Freedom wall is our choice for sentiment analysis since it provides chances for the sensitivity of articulated disposition