Natural Language Processing For Automatic Sentiment Analysis In Social Media Data

Siti Rahmawati; Dewi Anggraini; Rizki Kurniawan

doi:10.62951/ijies.v1i1.54

Authors

Siti Rahmawati Institut Teknologi Sepuluh Nopember (ITS)
Dewi Anggraini Institut Teknologi Sepuluh Nopember (ITS)
Rizki Kurniawan Institut Teknologi Sepuluh Nopember (ITS)

DOI:

https://doi.org/10.62951/ijies.v1i1.54

Keywords:

Natural Language Processing, Sentiment Analysis, Social Media, Machine Learning, Lexicon-Based, Deep Learning

Abstract

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.

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