What machine learning algorithm is used for sentiment analysis?

What machine learning algorithm is used for sentiment analysis?

There are multiple machine learning algorithms used for sentiment analysis like Support Vector Machine (SVM), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Random Forest, Naïve Bayes, and Long Short-Term Memory (LSTM), Kuko and Pourhomayoun (2020).

Which algorithm is used for text analysis?

There are many machine learning algorithms used in text classification. The most frequently used are the Naive Bayes (NB) family of algorithms, Support Vector Machines (SVM), and deep learning algorithms.

What type of data is used for sentiment analysis?

Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral. Sentiment analysis is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback, and understand customer needs.

What is the best algorithm for text classification?

Linear Support Vector Machine is widely regarded as one of the best text classification algorithms. We achieve a higher accuracy score of 79\% which is 5\% improvement over Naive Bayes.

Is Sentiment analysis part of NLP?

And, as we know Sentiment Analysis is a sub-field of NLP and with the help of machine learning techniques, it tries to identify and extract the insights.

Is Sentiment analysis NLP?

Sentiment Analysis (also known as opinion mining or emotion AI) is a sub-field of NLP that tries to identify and extract opinions within a given text across blogs, reviews, social media, forums, news etc.

Can sentiment analysis be done using both text and emoticons?

Earlier machine learning techniques only involve the classification of text, emoticons or images solely where emoticons with text have always been neglected, thus ignored lots of emotions. This research proposed an algorithm and method for sentiment analysis using both text and emoticon.

Which machine learning algorithms are used in sentiment analysis?

In the case of combined text and emoticon data, the sentiment was analyzed using some ML and DL algorithms such as support vector machine (SVM), NB, RF, logistic regression (LR), Long short term memory (LSTM) and convolutional neural network (CNN).

Can deep learning be used to extract sentiment from text?

Text analysis is one of the prominent researchable areas as it extracts sentiment using different ML and deep learning (DL) techniques with the help of recent technologies [5], [6]. But, research shows DL was rarely applied in SA on text and emoticon data combination.

What are emoticons and how to use them?

Over the years, people considered emoticons as a medium of communication that is used in texts or solely to dedicate one’s sentiment in an efficient manner. Emoticons are symbolic expressions consisting this type of tokens such as \\:”, \\=”, \\-”, \\)”, or \\ ( ” and commonly represent facial expressions.