Computer Science
Computer Science-2021.gz:143333
Computer Science
1aacbe52b6a0513c1058068189d778107e3a24ce
Sentiment Analysis : A Review and Comparative Analysis on Colleges

Sentiment analysis is the process of detecting positive and negative sentiment in text. It’s often u sed by businesses to detect sentiment in social data, gauge brand reputation, to understand it and make better. Sentiment analysis models focus on polarity (positive, negative and neutral) and even intentions (interested v. not interested). Depending on how you want to interpret feedback and queries, you can define and tailor your categories to meet your sentiment analysis needs. This paper focuses the reviews of college which are an important form of opinion contents. The basic objective of this work is to classify every sentence’s semantic orientation (e.g. positive, negative and neutral) of the reviews. It is a really useful analysis since we could possibly determine the overall opinion about the colleges.


I. INTRODUCTION
Nowadays the undeniable truth is that social media users are more likely to share how they feel about a current "hot topic" on social media platforms. It is current affairs that may trend regionally or globally.
Users may post positive, negative or neutral opinions about that topic or a particular product they are using. Applying deep learning method to sentiment analysis has also become very popular recently. But in practice, the long-range dependencies are still problematic to handle. So this paper presents maximum entropy for college reviews sentiment analysis.
Machine learning approach is proposed for the efficient classification of college data. To increase the efficiency of classification process, the technique parallel processing is implemented at each node.
After training the dataset the machine is analyses with various set of test data [3]. Every day massive amount of data is generated by users which can be used to analyze their opinion about any college.  Second, and then decide the actual probability by testing the dataset.

Support Vector Machine:
It is a supervised machine learning algorithm which can be widely used for both classification and regression challenges. And, SVM is better at computation speed and memory but worse at performance compared to deep learning.

Random Forest:
In a random forest algorithm, the set of many classification trees is generated by many decision trees taken together and proven effective for many sentiment tasks. This can be used mainly for regression and classification. The Decision Tree algorithm has rules for the given training data set with targets the features.

Decision Tree:
For classification the decision tree may be used with a structure like a tree. The best attribute of a dataset is put in a decision tree building algorithm, and then the training data set is divided into subsets. Decision trees are designed to generate a training model to predict the value of the destination variable.

Maximum Entropy:
It is a learning method based on empirical data and provides the probabilities for which sentence belongs to a particular class. While comparing to other it shows good result. Its principle is based on the most uniform significance method.
The key observations are shown in

VI. Application
Sentiment analysis is applicable to many real world business problems and marketing such as social media monitoring, twitter comment, customer feedback etc.
In the background of sentiment analysis, advanced AI algorithms apply language deconstruction techniques, like tokenization, part-of-speech tagging, parsing, and lemmatization to break down and make sense of text [9]. Only then can machine learning software classify unstructured text by emotion and opinion.
Sentiment Analysis is a term that includes many tasks such as sentiment extraction, sentiment classification, and summarization of opinions or opinion spam detection, among others. It aims to analyze people's sentiments, attitudes, opinions emotions, etc. towards elements such as, products, individuals, topics, organizations, and services.

Sentiment Analysis of College Reviews
In the current era reviews and opinions play crucial role in planning sees from claiming clients and also influence businessman to the success of a brand and service of products? The same things also apply when deciding the best among various alternatives we have [10]. The work in this report is to perform sentiment analysis on the colleges. Sentimental analysis ascribes to utilization of ML and is the formalization for studying and constructing sentiments.