Bangla Text Sentiment Analysis Using Supervised Machine Learning with Extended Lexicon Dictionary

Learning Domain-specific Sentiment Lexicon with Supervised Sentiment-aware LDA

A Supervised Method for Constructing Sentiment Lexicon in Persian Language

Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the imp...

Sentiment Analysis in Czech Social Media Using Supervised Machine Learning

This article provides an in-depth research of machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in case of Czech language there has not yet been any systematical research conducted. We tackle this issue and establish a common ground for fur...

Sentiment Analysis of Social Networking Data Using Categorized Dictionary

Sentiment analysis is the process of analyzing a person’s perception or belief about a particular subject matter. However, finding correct opinion or interest from multi-facet sentiment data is a tedious task. In this paper, a method to improve the sentiment accuracy by utilizing the concept of categorized dictionary for sentiment classification and analysis is proposed.  A categorized dictiona...

Lexicon-based Sentiment Analysis for Persian Text

The vast information related to products and services available online, of both objective and subjective nature, can be used to provide contextualized suggestions and guidance to possible new customers. User feedback and comments left on different shopping websites, portals and social media have become a valuable resource, and text analysis methods have become an invaluable tool to process this...