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“The social networking websites have gained enormous popularity. These websites provide platform for the people to express their views and share information among themselves. Currently, Sentiment Analysis is a topic of great interest. About 80% of world s

“Information sharing and review platforms has generated large volumes of opinionated data which is usually in unstructured form. With the help of Sentiment Analysis, this data can be transformed into structured data which can be useful for commercial appli

“This is the simplest form of sentiment analysis and it is assumed that the document contains an opinion on one main object expressed by the author of the document. Numerous papers have been written on this topic. There are two main approaches to document-

“Sentiment analysis (or Opinion mining) is defined as the task of finding the opinions of authors about specific entities. The decision-making process of people is affected by the opinions formed by thought leaders and ordinary people. When a person wants

“Traditional sentiment classification approaches use sentiment lexicons [10, 19, 20] to generate various features. Pang et al. treat sentiment classification as a special case of text categorization, by applying learning algorithms [14]. Many studies follo

“Mining sentiment from tweets has several applications in marketing, research and brand analytics. Organizations use tweet sentiment analysis to discover or analyze user feedback about products and services. Analysts in various domains may use it to conduc

“Previous studies have used many manually identified features and word embeddings for tweet sentiment classification. In this paper, we propose a new approach, which incorporates sentiment-specific word embeddings (SSWE) and a weighted text feature model (