Improving Generalization in Sentiment Analysis of Twitter Data with Logistic Regression Model
Keywords:
Sentiment analysis, Opinion mining, Natural language processing, Twitter dataAbstract
Sentiment analysis, commonly referred to as opinion mining, is an important problem in natural language processing that entails figuring out the sentiment represented in a document. Sentiment analysis of Twitter data has drawn a lot of attention as a result of the social media platforms' rapid expansion. Using logistic regression, a well-liked machine learning approach for binary classification applications, this research suggests a sentiment analysis system. The system starts by gathering and preprocessing a sizable Twitter dataset with tweets that have been labelled as positive or negative. By eliminating noise, stop-words, and unimportant information, the text data is cleaned. The techniques of tokenization and vectorization are used to represent the text in a numerical format appropriate for logistic regression. A suitable optimization approach is used to estimate the model parameters as the logistic regression model is trained on the labelled dataset. Cross-validation and performance indicators including accuracy, precision, recall, and F1-score are used to evaluate models. The system's goal for sentiment analysis jobs is high accuracy and reliable generalization
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