Sentiment Analysis can help to extract knowledge related to opinions and emotions from
user generated text information. It can be applied in medical field for patients monitoring …

In the specific field of sentiment analysis tasks, traditional machine learning models lack
effective attention to certain features. To address this issue, we extracted and classified user …

Fundamentally, a sentiment refers to the reflection of emotions of people. Today's world
stands on the strings of emotions. People express happiness, sadness, love, hatred etc …

Sentiment analysis can be used to derive knowledge that is connected to emotions and
opinions from textual data generated by people. As computer power has grown, and the …

Traditional text sentiment analysis methods often ignore context information when used in
the expression of features. The position of the words in the text makes it difficult to achieve …

Recognizing and categorizing user's sentiments from a part of text into different sentiments is
known as sentiment analysis. For instance, emotions such as happy, sad, angry or positive …

Sentiment analysis is concerned with extracting sentiment to ascertain the attitudes, and
emotions associated with the text. It is broadly applied to voice of the customer as they …

Microblogging websites like Twitter and Facebook, in this new era, is loaded with opinions
and data. One of the most widely used micro-blogging site, Twitter, is where people share …

Sentiment analysis is one of the fastest growing research areas in natural language
processing (NLP), making it challenging to keep track of all the activities. In a present day …

Sentiment analysis has been one of the most researched topics in Machine learning. The
roots of sentiment analysis are in studies on public opinion analysis at the start of 20 th …