Sentiment analysis or opinion mining involves systamtically extracting, identifying and quantifying subjective information using text natural language processing algorithms. The paper explores the recent research work in field of sentiment categorizes into five unique domains. This proposes a three stage framework involving data gathering, preparation phases. proposed is applied to tweets on Ru...

this study was an attempt to investigate the effect of lexicon-based debates on the felicity of lexical equivalents in translating literary texts by iranian efl learners.  to fulfill the purpose of this study, 59 university students, majoring in english translation, were randomly assigned to the experimental and control groups from a total of 73 students based on their performance on a mock toe...

Text mining studies have started to investigae relations between positive and negative opinions and patients’ physical health. Several studies linked the personal lexicon with health and the health-related behavior of the individual. However, few text mining studies were performed to analyze opinions expressed in a large volume of user-written Web content. Our current study focused on performin...

Sentiment word identification is a fundamental work in numerous applications of sentiment analysis and opinion mining, such as review mining, opinion holder finding, and twitter classification. In this paper, we propose an optimization model with L1 regularization, called ISOMER, for identifying the sentiment words from the corpus. Our model can employ both seed words and documents with sentime...

In this paper the authors seek to establish the most appropriate mechanism for conducting sentiment analysis with respect to political debates so as to predict their outcome. To this end two alternative approaches are considered, the classification based approach and the lexicon based approach. In the context of the second approach either generic or domain specific lexicons may be adopted, both...

The exponential increase in the explosion of Web-based user generated reviews has resulted in the emergence of Opinion Mining (OM) applications for analyzing the users' opinions toward products, services, and policies. The polarity lexicons often play a pivotal role in the OM, indicating the positivity and negativity of a term along with the numeric score. However, the commonly available domain...

This paper describes a method for automatically extending the lexicon of wide-coverage parsers. The method is an extension to the automatic detection of coverage problems of natural language parsers, based on large amounts of raw text (van Noord 2004). The goal is to extend grammar coverage, focusing in particular on the acquisition of lexical information for missing and incomplete lexicon entr...

Analysis of opinions, known as opinion mining or sentiment analysis, has attracted a great deal of attention recently due to many practical applications and challenging research problems. In this article, we study two important problems, namely, opinion lexicon expansion and opinion target extraction. Opinion targets (targets, for short) are entities and their attributes on which opinions have ...

This paper demonstrates that a large-scale lexicon tailored for the biology domain is effective in improving question analysis for genomics Question Answering (QA). We use the TREC Genomics Track data to evaluate the performance of different question analysis methods. It is hard to process textual information in biology, especially in molecular biology, due to a huge number of technical terms w...