Sentiment Analysis of the Twitter Dataset for the Prediction of Sentiments

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Abstract

Sentiment analysis of Twitter data involves using natural language processing (NLP) and machine learning techniques to classify the sentiments expressed in tweets. The goal is to classify tweets based on sentiment, emotion, or behavior in the text, using emotional labels such as positive, negative, or neutral. Twitter sentiment analysis is an important tool for instantly understanding public opinion on various topics, events, or brands. This process usually begins with the collection of large tweet data, followed by preliminary steps such as tokenization, outlier removal, and text normalization to clean the data. The text is then converted into a digital representation suitable for machine learning models using extraction techniques such as Bag-of-Words, Time-Inverse Document Frequency (TF-IDF), and word embedding. Deep learning models, such as convolutional neural networks (CNN), short-term neural networks (LSTM), and bidirectional LSTM (BiLSTM), are generally used to train and predict sentiment. This paper presents an effective method for sentiment analysis of Twitter profiles using deep learning methods, especially Convolutional Neural Networks (CNN), Long-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). The database contains 7000 tweets, which are pre-processed using text cleaning methods including tokenization, word removal, and lemmatization. The data is then converted to numerical vectors by methods such as bag-of-words and word embedding. The model is trained, and the test accuracy of CNN model is 0.95, test accuracy is 0.92, training accuracy of LSTM model is 0.97 and test accuracy is 0.90, and the training accuracy of BiLSTM model is 1.0 tab, and the accuracy rate is 0.9. . The results show a tendency to overdo it, with the model performing well on training data but poorly on test data. However, the model successfully classified tweets into positive, negative, and neutral groups, demonstrating the potential of deep learning in capturing sentiment from social media.