A Comparative Sentiment Analysis Of Sentence Embedding Using Machine Learning Techniques
A. Poornima, K. Kamakshi Priya
Abstract
Analyzing sentiment is a process to identify the opinion of a text. It is also known as opinion mining or emotion Artificial Intelligence (AI). People post comments in social media mentioning their experience about an event and are also interested to know if the majority of other people had a positive or negative experience on the same event. This classification can be achieved using Sentiment Analysis. Sentiment analysis takes unstructured text comments about a product reviews, an event, etc., from all comments posted by different users and classifies the comments into different categories as either positive or negative or neutral opinion. This is also known as polarity classification. Sentimental analysis can be performed by Text analysis and computational linguistics. This work aims at comparing the performance of different machine learning algorithms in performing sentiment analysis of Twitter data. The proposed method uses term frequency to find the sentiment polarity of the sentence. The performance of Multinomial Naive Bayes, SVM and Logistic regression algorithms in sentence classification were compared. From the results, it is inferred that logistic regression has achieved a greatest accuracy when it is used with n-gram and bigram model.