The literature in sentiment analysis has widely assumed that semantic relationships
between words cannot be effectively exploited to produce satisfactory sentiment lexicon …

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 …

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 …

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 …

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 …