Enhanced Social Media Sentiment Analysis

Abstract

Social media sentiment analysis is an important method of assessing the mood of the masses, internet trends as well as consumer mood. Use of social media applications such as Instagram, Facebook, and Twitter, creates a flood of unorganized informational content. The data are too sophisticated to be handled by simple data processing algorithms, but more sophisticated computational packages are required to generate valuable information that can be resistant to high dimensions, ambiguity, and noise. The given research introduces a novel methodology, which employs the use of machine learning and natural language processing to enhance the categorisation awareness, accuracy, scalability, and robustness of sentiment analysis systems in social media. Some of the preprocessing methods that the given solution employs to enhance the quality of the data are normalisation, removal of stop-words, cleaning text and tokenisation. One text processor that has the capability of transforming text to numbers is the Term Frequencyinverse Document Frequency (TF-IDF). Random Forest technique deals with an ensemble of learning models which are employed to perform a sentiment categorisation. This model can address complex patterns by alleviating overfitting. Everybody believes that the algorithm would be consistent in deciding whether anything on the social media is good, terrible, and neutral. We use measures such as F1-score, recall, accuracy, and precision so that the performance evaluation can be ensured to be workable across other datasets. Tests using superior frameworks indicate that the enhanced framework is better than the conventional machine learning frameworks, even in cases of imbalanced and noisy social media information. This system is scalable, interpretable and generalisable as compared to the past versions hence it is effective in real time solutions such as brand monitoring, customer feedback analysis, and decision support systems. On the whole, the proposed approach is feasible and efficient because it will enable the use of the unstructured information provided by the social media to draw the latest findings and breakthroughs in AI and sentiment analysis.