An Ensemble Based Approach for Sentiment Classification in Asian Regional Language

In today’s digital world,millions of individuals are linked to one another via the Internet and social media.This opens up new avenues for information exchange with others.Sentiment analysis(SA)has gotten a lot of attention during the last decade.We analyse the challenges of Sentiment Analysis(SA)in one of the Asian regional languages known as Marathi in this study by providing a benchmark setup in which wefirst produced an annotated dataset composed of Marathi text acquired from microblogging websites such as Twitter.We also choose domain experts to manually annotate Marathi microblogging posts with positive,negative,and neutral polarity.In addition,to show the efficient use of the annotated dataset,an ensemble-based model for sentiment analysis was created.In contrast to others machine learning classifier,we achieved better performance in terms of accuracy for ensemble classifier with 10-fold cross-validation(cv),outcomes as 97.77%,f-score is 97.89%.

A systematic study on the role of SentiWordNet in opinion mining

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms.These lexicons play an important role in performing several different opinion mining tasks.The efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the text.Therefore,one has to explore the available sentiment lexicons and then select the most suitable resource.Among available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion mining.In SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of classifiers.In this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive way.This survey will be useful for the researchers contributing to the field of opinion mining.Following features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers used.In the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.