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Which Algorithm is Used in Twitter Sentiment Analysis?
The question, “Which algorithm is used in Twitter sentiment analysis?” raises a lot of interesting questions. First, which dataset should you use for Twitter sentiment analysis? Tweets include not just words, but symbols, URLs, references to people, and emoticons. The words, however, are the most interesting part, as they contain a mix of misspelled words, extra punctuation, and repeated letters. To make a decent prediction, you should pre-process tweets.
Random Forest
The Random Forest algorithm is a machine learning technique that has recently gained widespread attention for its potential to improve the classification of tweets based on their sentiment. Using this technique, it is possible to determine the viewpoint of a writer and determine the overall emotional tone of a document. See the article : How to Make Your Twitter Public. In this study, we tested three prominent supervised learning classifiers on a dataset containing Tweets about movie reviews. We find that the Random Forest algorithm performs the best job of classifying Twitter sentiment.
The Random Forest algorithm is a popular technique used in Twitter sentiment analysis because of its versatility. The algorithm works by generating a large number of decision trees, each of which combines their data to form an overall decision. The trees are trained using different random samples (B), which vary in size. The Random Forest algorithm then applies this decision to the tweets, resulting in an improved classification accuracy. The Random Forest algorithm works because the data it is trained on is unrelated and has many potential uses.
Decision tree
A Decision tree algorithm is a popular machine-learning method for Twitter sentiment analysis. It classifies tweets based on their content and features. It can identify spam and neutral tweets, and is also effective for detecting rumors and false information. Read also : How to Mass Unfollow on Twitter. The proposed algorithm uses a large dataset, consisting of 31015 tweets, including 12548 positive tweets, 9732 negative tweets, and 8871 neutral tweets. To use it, the tweets must be pre-processed. The preprocessing stages include filtering, tokenization, and stemming/lemmatization.
To make a tweet’s polarity known, the first algorithm compares the words in the tweet to a database of positive and negative sentiment words. This database can be found online. For example, Kaggle provides a database of positive and negative words. The second algorithm uses a word-tokenizer, which is a popular natural language processing toolkit. By comparing words, the algorithm can determine whether a tweet is positive, negative, or neutral. With this algorithm, the accuracy was 46%, while chance was only 33%.
Multinomial naive Bayes
The multinomial naive Baye a classifier has been shown to perform well for Twitter sentiment analysis. This algorithm combines text processing with Laplace smoothing to improve its performance in predicting sentiment. Read also : When Was Twitter Launched?. Other varieties of Naive Bayes classifiers may do better, so we will explore these in the next lesson. Until then, we will look at the Multinomial naive Bayes algorithm to learn how to create a recommender system.
The multinomial naive Baye a model is a classification method that utilizes the Bayes theorem and the assumption of independence. It has been found to be effective for textual data analysis and Natural Language Processing (NLP). It has also been referred to as Independent Bayes or Simple Bayes. In a nutshell, a Naive Bayes classifier applies the Bayes theorem to classify a set of data based on its frequency.
Support vector machine
The Support Vector Machine (SVM) is an advanced statistical learning algorithm that was recently used in Twitter sentiment analysis. This technique uses clustering to identify and classify entities. However, the SVM algorithm fails to achieve this task with its current model due to several shortcomings. Among these weaknesses, it fails to produce high accuracy, and is prone to overfitting. Listed below are some advantages and disadvantages of SVMs and their use in Twitter sentiment analysis.
Social media sites have increased data production over the Internet. Because of this, a large number of users have a way to express their views. These users share their opinions with the help of “tweets.” These tweets may create a revolution for the better or a huge issue on any subject, and it is important to categorize them. However, a number of problems are associated with Twitter sentiment analysis.
