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Classification Approach for Sentiment Analysis Using Machine Learning
Abstract
A utilization of the computational semantics is known as natural language processing or NLP. Any opinion through attitude, feelings, and thoughts can be identified as sentiment. The overview of people against specific events, brand, things, or association can be recognized through sentiment analysis. Positive, negative, and neutral are each of the premises that can be grouped into three separate categories. Twitter, the most commonly used microblogging tool, is used to gather information for research. Tweepy is used to access Twitter's source of information. Python language is used to execute the classification algorithm on the information collected. Two measures are applied in sentiment analysis, namely feature extraction and classification. Using n-gram modeling methodology, the feature is extracted. Through a supervised machine learning algorithm, the sentiment is graded as positive, negative, and neutral. Support vector machine (SVM) and k-nearest neighbor (KNN) classification models are used and demonstrated both comparisons.
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