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This study evaluates the effectiveness of various machine learning and deep learning models, including logistic regression, random forest, naive bayes, gradient boosting, and LSTM networks, for sentiment analysis on Twitter data. By preprocessing a Kaggle Twitter dataset through tokenization, lemmatization, and stopword elimination, the LSTM model achieved a training accuracy of 90.98% and a testing accuracy of 80.00%, significantly outperforming traditional methods. The findings underscore the importance of advanced models in accurately capturing the contextual and sequential nuances of public sentiment in real-time social media discourse.
LSTM networks can achieve up to 90.98% training accuracy in sentiment analysis, far surpassing traditional machine learning methods.
In this age of social media, sites like Twitter have become meeting places for people to share their views and feelings on a wide range of issues and current events as they unfold in real time. Sentiment analysis, a critical application of NLP, has become indispensable due to the massive influx of user-generated content, enabling the extraction of meaningful insights from the opinions and emotions expressed in textual data. Sentiment analysis on Twitter employs sophisticated computational techniques to categorize tweets into positive, negative, or neutral sentiments. This method not only examines individual expressions but also analyzes vast databases related to specific subjects or events. By spotting these emotions, machine learning models help improve public opinion interpretation and trend forecasting. This paper examines the effectiveness of various machine learning and deep learning approaches. Designed for this use, the system evaluates logistic regression, random forest, naive bayes, gradient boosting, and LSTM networks, among other algorithms applied in sentiment classification. This work identifies the optimal sentiment analysis model using a Kaggle Twitter dataset that has been preprocessed through tokenization, lemmatization, and stopword elimination. Emphasizing the better performance of the LSTM approach, the model attained a training accuracy of 90.98%, a testing accuracy of 80.00%, and a micro-average ROC-AUC score of 0.92. These results show that the model outperforms conventional machine learning techniques in capturing contextual and sequential textual aspects.