Stopwords carry little semantic information and are often removed from text data to reduce dataset size and improve machine learning model performance. Consequently, researchers …
Twitter has brought much attention recently as a hot research topic in the domain of sentiment analysis. Training sentiment classifier from tweets data often faces the data …
We describe a state-of-the-art sentiment analysis system that detects (a) the sentiment of short informal textual messages such as tweets and SMS (message-level task) and (b) the …
A Kumar Jha - Available at SSRN 4150137, 2022 - papers.ssrn.com
Sentiment analysis has become a more active topic of research in recent years, thanks to the growth of social networks and ecommerce websites. Sentiment analysis, at its most basic …
C Eamwiwat, P Thanasutives, C Saetia… - … Joint Symposium on …, 2019 - ieeexplore.ieee.org
Sentiment analysis is an essential task for social listening, especially in service and product analysis. Prior works on sentiment analysis, especially in Thai language, mostly focus on the …
Sentiment is an important feature of natural language. It is used to understand semantic of texts and opinion of people. There are many practical applications, which require to extract …
Stop words represent frequently occurring words in a language that lack substantial semantic or pragmatic meaning. Consequently, they are often considered dispensable in …
M Machado, E Ruiz, KJ Abraham - arXiv preprint arXiv:1906.08717, 2019 - arxiv.org
Lexicon based sentiment analysis usually relies on the identification of various words to which a numerical value corresponding to sentiment can be assigned. In principle …