With the exponential growth of social media platforms, vast amounts of data are generated daily, capturing public opinions, sentiments, and trends in real time. Automatic sentiment analysis using Natural Language Processing (NLP) has emerged as an essential tool to process this data, helping industries, researchers, and policymakers understand social sentiment more effectively. This study explores various NLP techniques for sentiment analysis, including machine learning-based, lexicon-based, and deep learning models. By examining advancements in NLP algorithms and challenges related to language diversity, slang, and context in social media data, this paper highlights the strengths and limitations of current methodologies and discusses potential future directions.

The purpose of this study was to determine the effect of profitability, ownership structure, effectiveness of the audit committee, solvabilitas on timeliness in manufacturing companies in the consumer goods industry listed on the Indonesia Stock Exchange. The research year period used is 4 years, namely the 2016-2021 period. The population of this study includes all consumer-based industrial sector manufacturing companies listed on the Indonesia Stock Exchange (BEI) for the 2016-2021 period. The sampling technique used a population sampling technique. Based on the criteria set, 17 companies were obtained, the type of data used is secondary data obtained from the Indonesia Stock Exchange website. The analysis method used is panel data regression analysis. The results of the research simultaneously show that profitability, ownership structure, audit committee effectiveness and solvency have an effect on timeliness. Meanwhile, if partially tested, it shows that profitability, ownership structure, audit committee effectiveness and solvency have no significant effect on timeliness.