A Review on Evaluation of Different Models for Classifying Sentiments from Twitter: Challenges and Applications
Keywords:
Twitter Sentiment Analysis, NLP Techniques, Opinion Mining, Social media, Deep learning, Machine learningAbstract
Twitter has exploded in popularity in recent years as a place for users to discuss and share their thoughts on a wide variety of products and services. Researchers in the field of sentiment analysis have taken a keen interest in Twitter because of its rapid ascent to the top of the social media platform rankings. Businesses can learn a lot about their consumers' wants and needs and whether or not their products meet those wants and needs by conducting Sentiment Analysis. It's also used in healthcare to gauge public opinion on a drug or in politics to foretell the outcome of an election. Using NLP and ML techniques, Twitter Sentiment Analysis (TSA) is a helpful method for analyzing and categorizing the tone of tweets. Twitter's real-time data and potential uses in fields like advertising, brand management, and public opinion research have greatly contributed to its meteoric popularity. Previous studies have relied mostly on classical ML-based and lexicon-based approaches, rather than deep learning (DL) methods, for classifying emotional states in English tweets. In addition, a dearth of studies analyzes the polarity of tweets written in languages other than English, such as Arabic. When dealing with massive amounts of data, as is often the case with social network data, the deep learning approach has recently exhibited outstanding performance compared to typical ML algorithms. This article aims to give readers an introduction to DL for sentiment analysis on Twitter. The TSA task is first introduced briefly. We then discuss the TSA task's framework from multiple angles, focusing on Twitter sentiment analysis. In this article, we will go through the different methods for conducting sentiment analysis and their uses and difficulties.
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