Publication Date: 2023/12/18
Abstract: Sentiment analysis is a subset of text analysis techniques that uses automatic text polarity detection. One of the main responsibilities of NLP (Natural Language Processing) is sentiment analysis, often known as opinion mining. In recent years, sentiment analysis has gained a lot of popularity. It is meant for people to build a system that can recognize and categorize sentiment or opinion as it is expressed in an electronic text. Nowadays, people who wish to purchase consumer goods prefer to read user reviews and participate in public online forums where others discuss the product. This is because consumers frequently have to make trade-offs when making purchases. Before making a purchase, a lot of customers read other people's reviews. Individuals frequently voice their opinions about several things. Opinion mining has grown in significance as a result. Sentiment analysis is the process of determining if the expressed opinion about the subject is favorable or negative. Customers must choose which portion of the available data to utilize. Sentiment analysis is the technique of locating and removing subjective information from unprocessed data. If we could accurately forecast sentiments, we could be able to gather online opinions and anticipate the preferences of online customers. This information could be useful for study in marketing or economics. As of right now, sentiment classification, feature-based classification, and handling negations are the three main issues facing this research community.
Keywords: Numpy, Pandas, TF-IDF, Tfidf Vectorizer, Linear SVC, Train-Test Split, Accuracy Score, Classification Report, Confusion Matrix, user Input, Vectorization, Prediction, Preprocessing, Text Classification, Supervised Learning, Machine Learning Model, Scikit-Learn.
DOI: https://doi.org/10.5281/zenodo.10401483
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT23DEC522.pdf
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