a supervised method for constructing sentiment lexicon in persian language

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...

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...

A Morphological Lexicon for the Persian Language

We introduce PerLex, a large-coverage and freely-available morphological lexicon for the Persian language. We describe the main features of the Persian morphology, and the way we have represented it within the Alexina formalism, on which PerLex is based. We focus on the methodology we used for constructing lexical entries from various sources, as well as the problems related to typographic norm...

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

Constructing An Automatic Lexicon for Arabic Language

In this paper, we have designed and implemented a system for building an Automatic Lexicon for the Arabic language. Our Arabic Lexicon contains word specific information. These pieces of information include; morphological information such as the root (stem) of the word, its pattern and its affixes, the part-of-speech tag of the word, which classifies it as a noun, verb or particle; lexical attr...

ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking

This paper describes our system submissions to task 7 in SemEval 2016, i.e., Determining Sentiment Intensity. We participated the first two subtasks in English, which are to predict the sentiment intensity of a word or a phrase in English Twitter and General English domains. To address this task, we present a supervised learning-to-rank system to predict the relevant scores, i.e., the strength ...